Gnss forecast impacting receiver startup

ABSTRACT

Disclosed is reducing starting time for a GNSS receiver that has an imprecise initial starting location by requesting starting assistance from a CDN that caches predictive data including first data indicated predicted LOS visibility from the receiver to individual satellites, wherein the request includes the imprecise initial staring location, receiving, from the CDN, data that includes a first block of the predictive data for the imprecise initial staring location and further adjoining second blocks of predictive data for areas surrounding the imprecise staring location, determining, by the GNSS receiver, commonly available satellites that have visibility from locations in both the first block and the second block, and calculating a first starting position using weighted values for the satellites, the commonly available satellites having higher weighted value than satellites without visibility in both locations, whereby position uncertainty of the first starting position is reduced from the imprecise initial starting location.

PRIORITY

This application claims priority to and the benefit of U.S. 63/407,589titled “Accuracy of a GNSS Receiver That Has a Non-Directional Antenna,filed 16 Sep. 2022 (Attorney Docket No. SPIR 1169-1).

This application is also a continuation in part of U.S. application Ser.No. 17/706,421 titled “Enhancing RTK Position Resolution Using AnRTK-Enabled GNSS Positioning Receiver,” filed 28 Mar. 2022 (AttorneyDocket No. SPIR 1139-11) which is a continuation U.S. application Ser.No. 17/374,885 titled “Enhancing RTK Position Resolution Using AnRTK-Enabled GNSS Positioning Receiver,” filed 13 Jul. 2021, now U.S.Pat. No. 11,287,531, issued 29 Mar. 2022. which claims priority to U. S.Provisional Patent Application Nos. 63/051,849 entitled “An Architecturefor Providing Forecasts of GNSS Obscuration and Multipath,” (AttorneyDocket No. SPIR 1139-1) filed 14 Jul. 2020 and 63/161,386 entitled “AnArchitecture for Providing Forecasts of GNSS Obscuration and Multipath,”(Attorney Docket No. SPIR 1139-3) filed 15 Mar. 2021. The priorityapplications incorporated by reference for all purposes.

RELATED CASES

This application is related to the following contemporaneously filedapplications:

U.S. application Ser. No. 17/948,176 titled “GNSS Forecast andSpoofing/Jamming Detection,” filed 19 Sep. 2022 (Attorney Docket No.SPIR 1169-3); and

U.S. application Ser. No. 17/948,182 titled “GNSS Forecast andBackground Obscuration Prediction,” filed 19 Sep. 2022 (Attorney DocketNo. SPIR 1169-4); and

U.S. application Ser. No. 17/948,190 titled “GNSS Forecast and Line ofSight Detection” filed 19 Sep. 2022 (Attorney Docket No. SPIR 1169-5);and

U.S. application Ser. No. 17/948,182 titled “Utilizing GNSS RiskAnalysis Data for Facilitating Safe Routing Of Autonomous Drones” filed19 Sep. 2022 (Attorney Docket No. SPIR 1164-2); and

U.S. application Ser. No. 17/948,190 titled “Generating and DistributingGNSS Risk Analysis Data for Facilitating Safe Routing Of AutonomousDrones” filed 19 Sep. 2022 (Attorney Docket No. SPIR 1164-3).

The related applications are incorporated by reference for all purposes.

INCORPORATIONS

The following materials are incorporated by reference for all purposesas if fully set forth herein:

U.S. application Ser. No. 17/374,882 entitled “Accuracy Of A GNSSReceiver That Has a Non-Directional Antenna,” (Attorney Docket No. SPIR1139-5) filed 13 Jul. 2021 and

U.S. application Ser. No. 17/374,891, titled “Path Planning UsingForecasts Of Obscuration And Multipath” filed 13 Jul. 2021 (Atty DocketNo, SPIR 1139-6); and

Recommendation ITU-R P.681-11 (August 2019), Propagation data requiredfor the design systems in the land mobile-satellite service; and

Recommendation ITU-R P.681-11 (August 2019), Propagation data requiredfor the design systems in the land mobile-satellite service; and

Report ITU-R P.2145-2, (September 2017), Model parameters for thephysical-statistical wideband model in Recommendation ITU-R P.681; and

Recommendation ITU-R P.1407-7, (August 2019), Multipath propagation andparameterization of its characteristics; and

GB Application No. 1111305.7, titled Recording, Storage and Playback ofGNSS Signals, filed 4 Jul. 2011, now GB Patent No. 2492547, issued 7Nov. 2018 (Atty Docket No. SPIR 1134-1 GB); and

U.S. application Ser. No. 13/786,020, titled System and Method forTesting Real World A-GNSS Performance Of A Device, filed 5 Mar. 2013,now U.S. Pat. No. 9,519,063, issued 13 Dec. 2016 (Atty Docket No. SPIR1071-1); and

Federal Aviation Administration (FAA) Technical Standard Order(TSO)-C199 for Traffic Awareness Beacon System (TABS)

FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed relates to data processing, vehicles,navigation and relative location. The technology disclosed provides forelectrical computers, digital data processing systems, and dataprocessing processes for transferring data between a plurality ofcomputers or processes wherein the computers or processes employ thedata before or after transferring and the employing affects the transferof data therebetween.

In particular, the technology disclosed relates to improving the startuptime and accuracy of GNSS receivers. The disclosed technology alsoincludes recognizing and rejecting spoofed or jammed GNSS signalsreceived by a GNSS receiver. The disclosed technology also includesdifferent resolutions for near and distant locations. The disclosedtechnology also includes efficient resolutions for establishing masksfor LOS.

BACKGROUND

The subject matter discussed in this section should not be assumed to beprior art merely as a result of its mention in this section. Similarly,a problem mentioned in this section or associated with the subjectmatter provided as background should not be assumed to have beenpreviously recognized in the prior art. The subject matter in thissection merely represents different approaches, which in and ofthemselves may also correspond to implementations of the claimedtechnology.

Connected Autonomous Vehicles (CAV) and vehicles with AdvancedDriver-Assisted Systems (ADAS) require reliable GPS sensor information.In one example, an autonomous vehicle needs to have reliable positioninginformation for safety. The absence of good GPS signal coverage cancause malfunctions in the decision-making logic of an automated orself-driving car, causing it to navigate incorrectly and potentiallycollide with traffic. In another example, for an Unmanned Aerial System(UAS/drone) being used for package delivery or survey relies uponconsistent highly accurate GPS positioning. The result of encounteringan area of poor GPS coverage can be the failure of a package delivery,non-compliance with Remote Identification requirements, non-conformanceto the approved operational airspace, or even increased risk ofcollision with other aircraft.

Simulations can be performed before equipment is used in a live setting,in order to minimize risks involved in component or system failure.Testing for receiver equipment exists and is widely used in theindustry. Simulation and testing in a design environment ensures thatthe system has a known performance given a range of signal qualities andtypes. Modeling and simulation of a signal environment specific to anarea of operation is less prevalent.

An opportunity arises for providing dilution of precision (DOP)forecasts for GNSS navigation for routing of vehicles or alerting humansin vehicles, distributing forecasts with more information than a devicecan estimate from its sensing of the environment that it is in.Moreover, the forecast is done in advance of a vehicle being in theenvironment and hence can be used for planning and improving integrity.The disclosed technology can improve both real time and route planningfor both terrestrial and airborne vehicles, providing improvedinformation about the reliability of the signals being processed by GNSSreceivers, and providing the predictions over an Internet cloudarchitecture to millions of users.

SUMMARY

The technology disclosed addresses improving accuracy of a GNSS receiverthat has a non-directional antenna including sending a request forpredictive data for an area that includes the receiver to a CDN.Responsive to the query, the technology includes receiving dataindicating predicted line-of-sight visibility from the receiver toindividual satellites and extracting from the data a prediction for apresent or future location of the receiver and using the extracted datafor satellite selection, for choosing some and ignoring other individualsatellites.

Particular aspects of the technology disclosed are described in theclaims, specification and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve only toprovide examples of possible structures and process operations for oneor more implementations of this disclosure. These drawings in no waylimit any changes in form and detail that may be made by one skilled inthe art without departing from the spirit and scope of this disclosure.A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The color drawings also may be available in PAIRvia the Supplemental Content tab.

FIG. 1 shows example architecture for improving accuracy of a GNSSreceiver that has a non-directional antenna, according to one embodimentof the disclosed technology.

FIG. 2 shows an alternate implementation in which GNSS Forecasts arestored directly from the forecast engine to the CDN.

FIG. 3A shows a detailed block diagram of system architecture forimproving accuracy of a GNSS receiver that has a non-directionalantenna.

FIG. 3B shows a message flow for interactions of forecast worker service(FWS) 356 with other services.

FIG. 3C shows an example bounding box, usable to facilitate theselection of Forecast Batch data that correspond to a particular userpoint/trajectory/area.

FIG. 3D shows a sketch of a stack of shaded tiles and an example 1 Km²output buffer of FES.

FIG. 3E illustrates an Amazon Web Services (AWS) Cloud implementation ofthe disclosed architecture, showing a detailed block diagram of thecomponents and protocols of system architecture.

FIG. 3F describes an example forecast file for a given area (tile) overa time period.

FIG. 4A illustrates a 2D map orbit with GNSS satellites, and an exampleerror calculation for a 2D point.

FIG. 4B illustrates an example 1-pass analysis for finding satellitevisibility at a Valid Observable Point (VOP).

FIG. 4C illustrates the 1st pass of an example 2-pass analysis forfinding satellite visibility at a Valid Observable Point (VOP).

FIG. 4D illustrates the 2nd pass of an example 2-pass analysis forfinding satellite visibility at a Valid Observable Point (VOP).

FIG. 5 shows message flow for session authentication between theuser/customer 348, content delivery network (CDN) and GNSS Forecastengine service (FES) that provides forecasts to the CDN.

FIG. 6A shows the block diagram for a receiver, with the flow of datafrom the Forecast Assured Navigation (FAN) system to elements in theGNSS receiver.

FIG. 6B illustrates an example result of utilizing the disclosed GNSSForecast with RTK to improve performance.

FIG. 6C illustrates SNR of GPS signals at corresponding elevations on anopen field, using real world data.

FIG. 6D illustrates a scenario in which a large obstruction is distantfrom nearby grid of points and is still accounted for by the forecastsystem.

FIG. 7A illustrates one example of the disclosed Forecast AssuredNavigation (FAN) user interface.

FIG. 7B illustrates the relationship between accuracy and precision fortarget satellite locations, for evaluating and visualizing navigationcorridors.

FIG. 8A illustrates the effect of turning a geometric corner in an urbanenvironment on the number of LOS satellites within a target area.

FIG. 8B shows an example urban roadway for utilizing FAN Forecasts for asmart intersection, illustrating the need for accurate GNSS Forecasts,pictorially.

FIG. 8C illustrates the environment of fast convergence for low initialuncertainty, illustrated in an urban environment, and a first approachto efficient startup convergence.

FIG. 8D illustrates a second approach to convergence by correlatingsatellite visibility data with signal strength by the receiver.

FIG. 9A shows the entry of a proposed route via the route button or byimporting of a KML file for expressing geographic annotation andvisualization, by selecting the “create a route” feature.

FIG. 9B shows the completed route before the user selects finish todisplay route information. The solid line illustrates the intendedpath/trajectory.

FIG. 9C shows the route length and the enabled DOP layer displays theGNSS forecast heat map and subsequent too-wide flight corridor, due toposition accuracy requirements.

FIG. 9D shows the visualization interface with the DOP layer feature on,in 2D side view.

FIG. 9E shows the route map as it is entered, and the flight corridor iscalculated and displayed alongside the route, in both top view and sideview.

FIG. 9F shows the visualization UI with a completed route with the GNSSforecast data informing the route via the displayed heat map in whichdifferent DOP ranges are displayed in different shadings, to avoiddegraded performance and ensure the tightest possible flight corridor.

FIG. 9G shows a UI view of the visualization interface that allows usersto plan a route by clicking on the map while the heat map is visible.

FIG. 10A shows a 3D visualization of the forecast interface displayingthe GNSS Forecast DOP in measure mode for a location selected by theuser.

FIG. 10B shows the 3D visualization UI with rays enabled for viewingindividual lines of sight from reticule to satellites in orbit.

FIG. 11 displays the GNSS Forecast at 20:23:00 GPS at ground level.

FIG. 12 displays the GNSS Forecast at 20:47:18 GPS at ground level.

FIG. 13 displays the GNSS Forecast at 20:47:18 GPS at 30 meters aboveground level.

FIG. 14 , FIG. 15 and FIG. 16 illustrate GNSS Forecast visualizations atthree sequential times, separated by 12 minutes each.

FIG. 14 depicts a route along West Ohio Street in the urban center ofIndianapolis that shows where the receiver thinks it is, at 12 minutesbefore the mid-point time of the drive, encoded with PDOP value bands.

FIG. 15 depicts 3D GNSS Forecast visualization heat map results for WestOhio Street, Indianapolis, at four different altitude planes.

FIG. 16 depicts floating planes of signal strength for visualizingsignal coverage over time, showing a lateral view of 3D GNSS Forecastvisualization results for West Ohio Street, Indianapolis, at fourdifferent altitude planes.

FIG. 17 illustrates an example of a customer request, with a four-pointpolygon and min/max heights.

DETAILED DESCRIPTION

The following detailed description is made with reference to thefigures. Sample implementations are described to illustrate thetechnology disclosed, not to limit its scope, which is defined by theclaims. Those of ordinary skill in the art will recognize a variety ofequivalent variations on the description that follows.

Autonomous or assisted navigation often depends on positioninginformation from satellite constellations, from GPS, in the UnitedStates, and GNSS, more generically. There are many known problems withGNSS positioning, related to obscured signals and multi-pathing.Estimates of the reliability GNSS positioning are important toautonomous vehicles, both ground vehicles and flying vehicles.

Urban and suburban areas present one class of problems. Tall buildingsboth obscure signals, blocking the line of sight to satellites, andcause multi-pathing. A GNSS receiver in an urban canyon can see only anarrow slice of the sky. It still receives signals from many satellites,reflected off buildings and flat surfaces. However, the distancetraveled changes when signals are reflected, as opposed to signalsreceived on a line of sight. GNSS receivers strive for accuracy within 3feet. This accuracy is difficult to obtain for a reflected signal thattravels 100 feet further than a line-of-sight signal would.

Even rural areas are subject to obscure signals and multi-pathing.Trees, for instance, partially obscure satellite signals. Hilly ormountainous countryside surfaces can induce multi-pathing. These effectsare less dramatic in rural areas than in urban canyons.

Conditions between the satellite and receiver can partially obscure anddisrupt signals. These conditions include both weather in the atmosphereand energizing of the ionosphere by solar disturbances.

One approach to signal reliability has been crowd sourcing of receiverestimates of locations from actual vehicles. The problem is thatreceivers sometimes are biased towards being overly confident abouttheir position estimate. Moreover, the satellites are constantlyorbiting around the earth and while a receiver may experience a goodsignal at one location and time, it may not in the future.

In one example, Uber uses a client-server architecture that utilizes 3Dmaps and performs sophisticated probabilistic computations on GPS data.Statistical modeling in a probabilistic framework, for checking whetherthe ray joining the location to the satellite is blocked for a possiblelocation for the receiver, can utilize crowd-sourced 3D maps. Theprobability distribution model of the SNR under LOS and shadowedcondition is usable for determining the likelihood of the SNR measuredfor the satellite, and this can be duplicated over a grid of possiblelocations to obtain a likelihood surface—or heat map—of possiblereceiver locations, based on satellite signal strengths, forprobabilistic shadow matching. The heat map from probabilistic shadowmatching has very many local maxima and the GNSS fix can have largeoutliers, driving the use of filters to approximate arbitrarydistributions, at the expense of high complexity. The accuracy is alsolimited by the quality of the crowd-sourced 3D maps.

Limitations introduced by the use of statistical models motivate thedisclosed technology for forecast assured navigation (FAN) for providingdilution of precision (DOP) forecasts and/or degree of confidenceforecasts for GNSS navigation for routing of vehicles or alerting humansin vehicles, using simulation for generating prediction of GPS/GNSSsignals based on time, position, environmental data and maps. Thedisclosed GNSS Forecast heat maps are usable to determine integrity andperformance along a route and for nearby areas.

Acronyms

Acronyms used in this disclosure are identified the first time that theyare used. These acronyms are terms of art, often used in standardsdocuments. Except where the terms are used in a clear and distinctlydifferent sense than they are used in the art, we adopt the meaningsfound in testing standards. For the reader's convenience, many of themare listed here:

ADAS Advanced Driver-Assisted System AGL Above Ground Level APIApplication Programming Interface ASIL Automotive Safety Integrity LevelAWS Amazon Web Services BVLOS Beyond Visual Line of Sight CAV ConnectedAutonomous Vehicles - self-driving cars and autonomous drones CMSContent Management System CDN Content Delivery Network CDNG CDN GatewayDAV Detect and Avoid DCM Digital City Map DEM Digital Elevation ModelDSM Digital Surface Model - earth's surface and objects on it DOPDilution of Precision DTM Digital Terrain Model ERP Enterprise ResourcePlanning eVTOL Electric Vertical Takeoff and Landing F2CDNS Forecast toCDN Service FAN Forecast Assured Navigation FBX Autodesk FilmBox Format(3D Data Interchange) FCaaS Flight Check as a Service FC ForecastController FCBS Forecast Call-back Service FES Forecast Engine ServiceFO Forecast Orchestrator FPS Forecast Processing Store FWS ForecastWorker Service GIS Geographical Information System GNSS GlobalNavigation Satellite System GPS Global Positioning System GPU GraphicsProcessing Unit gRPC Open-source Remote Procedure Call HAV Hybrid AirVehicles IaaS Integrity as a Service JSON JavaScript Object Notation LESLogging and Event Service LOS Line of Sight MCS Map Curation Service MPPMap Processing Pipeline NLOS Non-Line of Sight NTP Network Time ProtocolODD Operational Design Domain OPR Orbit Prediction Runner OPS OrbitPrediction Service OSR Observation Space Representation P2CDNS Public toCDN Service PDOP Position Dilution of Precision PE Positioning EnginePNT Position, Navigation and Time POC Proof of Concept PosAppPositioning Application - Proprietary Spirent software application formulti- channel satellite navigation (GNSS) simulation systems PPPPrecise Point Positioning RAIM Receiver Autonomous Integrity MonitoringRDVS RINEX Downloader & Validator Service REST Representational StateTransfer RINEX Receiver Independent Exchange Format RM Road Map RNAVArea Navigation RNP Required Navigation Performance RPC Remote ProcedureCall RTK Real-Time Kinematic SCS Schedule Curation Service SDSPSupplemental Data Service Provider SHP Shape File - commercial standardfor representing geospatial vector data SimGEN Mode of PosApp: fullscenario environment development capability; extended data input/outputoptions, user actions and commands; advanced remote control SLA ServiceLevel Agreement SNR Signal to Noise Ratio SQL Structured Query LanguageSS Scheduler Service SSR State Space Representation TDOA Time Differenceof Arrival - multilateralization for geolocation of RF emitters TRANSSECTransmission Security TSE Total System Error TTA Time to Alarm UAUnmanned Aircraft UAAA Urban Advance Air Mobility UAS Unmanned AerialSystem UAV Unmanned Aerial Vehicles USS Unmanned Service Supplier UTMUniversal Transverse Mercator coordinate system UTMP Unmanned TrafficManagement Program VMPS Vendor Map Processing Service VOP ValidObservable Points WAAS Wide Area Augmentation System WGS84 U.S. DoDglobal reference system for geospatial information for GPS

The Forecast Assured Navigation (FAN) technology disclosed hereinaddresses the need of GNSS receivers, measurement engines, positioningengines, consumer devices, telecommunication equipment, navigationsystems, avionics, and vehicles' “clients” that use GNSS as a source ofposition, navigation and timing to know where and when GNSS signals willbe available, impaired, or not available. Clients can exist in alaboratory environment for test and validation, in a planning systembefore operation of a system, or in a live environment where the clientis in an operational vehicle. Existing integrity and augmentationtechniques consider many variables, such as the atmosphere, satelliteerrors and orbits, to improve the performance and reliability of theclient. The local environment, such as buildings, are not known to theclient and are one of the primary unaddressed sources of errors due toloss of the signal due to obscuration or interference of the signal dueto partial obscuration of the signal and multipath. When the client isgiven a prediction of GNSS obscurations and multipath, the client caneither plan to avoid areas or use the prediction to improve performanceof the client.

Autonomous vehicles can benefit from improved information about thereliability of the signals being processed by GNSS receivers. Thisinformation is useful both in real time and for route planning. Theprocess of generating a prediction of GPS/GNSS signals based on time,position, environmental data, and maps uses various methods to determinesatellite obscurations based on position, time, environmental data, andmaps. Moreover, the process uses various methods to determine satellitesignal degradation due to multipath and other interference based onposition, time, environmental data and maps.

The disclosed technology for Forecast Assured Navigation (FAN) usesenvironmental data (maps containing building locations, terrain,vegetation, and other obstacles) to determine GNSS satelliteobscurations (line-of-sight, non-line-of-sight, out-of-view) at sometime in the past, present, or future and for specific locations, alsoreferred to as a GNSS obscuration forecast.

The disclosed technology also uses environmental data (maps containingbuilding locations, terrain, vegetation, and other obstacles) todetermine GNSS satellite multipath at some time in the future and forspecific locations, referred to as a GNSS multipath forecast.

The disclosed technology applies statistical multipath models to 3D mapsthat represent a real environment. Existing models detailed in ITU-RP681-11, P.2145-2, and P.1407-7 are designed to model multipath forsatellite communications, not GNSS. The disclosed technology can extendthese and other models for use in GNSS despite the fact that they do notuse as many satellites or the same exact frequencies. Moreover, existingmodels use self-generated maps that are synthetically created torepresent different types of environments such as urban, suburban, ruraland other types of environments. The disclosed technology uses the samealgorithms, but instead of self-generated maps uses 3D maps thatrepresent a real environment to generate a prediction of GNSS satellitemultipath signals.

The disclosed Forecast Assured Navigation (FAN) technology features anobscuration forecast and a multipath forecast, that enable determiningin advance where and when GNSS is reliable. FAN is a cloud-based servicewhich provides additional forecast information about the expected GNSSvisibility for specific regions around the world, as requested by theend user. This service differs from existing solutions as it alsoconsiders the terrain and the built-up environment instead of assumingopen sky. SaaS supports receivers, positioning engines and navigationsystems to provide better situational awareness of the GNSS signals theyare receiving. The additional information is computed by applicant usingpresent satellite orbits, GNSS simulators and high-definition 3D maps tolook into the future and provide the intended solution with a much moreprecise expected satellite signal visibility than would be availableotherwise. This technology supports features for autonomous ground-basedvehicles for real-time planning and operation, including automated lanecontrol with or without a known path, and for mission planning forfuture routes.

A system architecture for improving accuracy of a GNSS receiver that hasa non-directional antenna is described next.

System Architecture

FIG. 1 shows example system architecture 100 for improving accuracy of aGNSS receiver that has a non-directional antenna. The architectureincludes the design and interfaces for gathering satellite data,environmental data, maps and algorithms, and for storing data via datamanagement, distributing on a cloud architecture and interfacing withusers. Because FIG. 1 is an architectural diagram, certain details areintentionally omitted to improve clarity of the description. Thediscussion of FIG. 1 is organized as follows. First, the elements of thefigure will be described, followed by their interconnections. Then, theuse of the elements in the system will be described in greater detail.

System architecture 100 includes applicant's cloud 155 with GNSSForecast engine service (FES) 125, map engine and service 128, GNSS CDNservice 158, visualization block 152 and end user/customer positioningengine 195. GNSS Forecast engine service (FES) 125 interfaces with theenvironmental data module and simulation/algorithm module and calculatespredictions of GNSS signals for geographically defined areas. Thepredictions from the GNSS Forecast engine service (FES) 125 are storedand then published to the cloud architecture. Applicant's cloud 155includes GNSS Forecast engine service (FES) 125 that utilizes data fromforecast models 102 in conjunction with satellite information 116provided by correction service 106. Forecast models 102 include 3D RFmodels 112 and satellite atmosphere models POSApp and SimGEN 122. GNSSForecast engine service (FES) 125 receives map info fusion/GNSS 135 viamap engine and service 128, and stores and retrieves GNSS Forecasts inSpirent's cloud database 144 as part of data management 132, which alsoincludes historical data 142. Data sync engine 146 provides GNSSForecasts to cloud database 168 in GNSS CDN service 158, as part of datamanagement 132. Map engine and service 128 also includes 3D maps 138 andbase maps 148 among others maps. GNSS CDN service 158 includes queryhandling 176 via API 177 to customer app 186 in end user/customerpositioning engine 195, which also handles real time data 196 fromcustomer positioning engine 195. GNSS CDN service 158 also handles GNSSForecast subscriptions via subscription management 178 controlled byCMS/ERP 169. Additionally visualization block 152 interfaces withcustomer positioning engine 195 via web interface 182 via localvisualization engine 164, cloud service visualization 166 and GIS layer162. Local visualization engine 164 displays forecasts stored inapplicant cloud database 144 by GNSS Forecast engine service (FES) 125in one implementation of the system.

The disclosed cloud architecture provides a globally distributed, lowlatency, high-availability system for clients to request GNSS Forecasts.Clients can request the GNSS predictions for several applicationsincluding, but not limited to GNSS receivers, measurement engines,positioning engines, consumer devices, telecommunication equipment,navigation systems, avionics, and vehicles for planning, operation, orvalidation. The disclosed Forecast Engine Service (FES) inputs a map andsatellite orbits, and outputs a forecast of LOS/NLOS plus PDOP for each1 m² point within the map, in one implementation.

FIG. 2 shows the block diagram for a system implementation in which GNSSForecasts are stored directly from the forecast engine to the CDN, analternative to redundant data management described relative to FIG. 1 .The block diagram of GNSS Forecast architecture 205 utilizes multiplemodules. User upload 218 provides inputs to map processing pipeline 215as digital surface model (DSM) of the earth's surface and objects on it,and shapefile (SHP) that represents shapes as geospatial vector data.Map processing pipeline 215 completes the initial map processing andoutputs 3D map data in FBX 3D data interchange format (FilmBox), alongwith at least one metadata file that describes the map data. The FBX isa 3D model mesh suitable for loading into a GPU and the metadata filecontains geographic information about the FBX such as where the FBX“tile” is located on the earth's surface and a list of valid observablepoints (VOPs). FBX tiles can be different sizes. Processing to 1 Km²utilizes VOPs in a grid of cuboids at 1 m intervals, in oneimplementation. When creating VOPs, the building footprints aretypically masked to not include VOPs inside of buildings. An automateddaily upload 228 provides to orbit prediction service 225 the GPSalmanac data set that every GPS satellite transmits, which includesinformation about the state of the GPS satellite constellation, as wellas coarse data on every satellite in orbit. The orbital position of eachsatellite is known as the ephemeris data. Automated daily upload 228provides the raw satellite navigation system data in ReceiverIndependent Exchange Format (RINEX). Orbit prediction service 225handles the initial orbit prediction service and sends the orbitprediction data needed by forecast orchestration 252, in the formatspecified via API 224.

Continuing the description of GNSS Forecast architecture 205, GNSSForecast engine service (FES) 125 generates GNSS Forecast data, via GPUray casting on FBX data with metadata, and provides the forecast data toGNSS Forecast orchestration 252 as specified via forecast API 244 viaforecast service call back (FSCB). Management of GNSS Forecasts fromadministrator and customer side can be done over Internet browserservices. A mobile interface could be utilized in anotherimplementation. GNSS Forecast engine service (FES) 125 generates GNSSForecast data for defined cuboids. Each cuboid has unique ID (GUID).Cuboids get uploaded to GNSS Forecast CDN 255 using forecast input API254 via PostGIS software program that adds support for geographicobjects to the PostgreSQL object-relational database, in oneimplementation. CDN 255 provides the 3D forecast visualization from GNSSForecast orchestration 252 for display with browser visualization 258,via customer API 256. Forecast API 244 enables GNSS Forecast CDN 255 tocommunicate, to GNSS Forecast engine service (FES) 125, on-demandrequests or other requests for data that CDN 255 was not able to provideto the device, in some embodiments. In summary, GNSS Forecastarchitecture 205 delivers GNSS Forecasts via content delivery network255 to a user/customer of the forecasts. When a user makes a request fordata, if CDN 255 does not have a specific cuboid in the CDN database, itcreates a new entity. If a cuboid with the defined ID is available, thesystem can add a new time forecast or update an existing one. Cuboidsare provided from GNSS Forecast engine service (FES) 125. In case ofcollision, the previous cuboid, in time, will be replaced. A cuboid canbe sliced and modified by GNSS Forecast data processor, if needed. Theforecast data can be processed prior to sending to the customer in someimplementations.

The disclosed GNSS Forecast engine service (FES) 125 inputs a map andsatellite orbits and outputs a forecast of line-of-sight,non-line-of-sight (LOS/NLOS) plus PDOP for each 1 m². FES provides theability to determine probability, ranking, and scoring of LOS/NLOS,out-of-view GNSS satellites in a GNSS obscuration forecast, usingsatellite orbit and atmospheric models from Spirent SimGEN/POSApp andtreating each satellite as a transmission point in the sky. Thedisclosed technology uses a surveyed global 3D map with shape files anddigital city maps (DCM) which show the dimensions of buildings, terrain,vegetation, and other obstacles to ray-trace between selected points onthe map, to determine if each satellite is blocked/obscured by objectsin the 3D map that is based on the real world. The disclosed technologyuses surveyed maps. Part of the challenge in that is theaccuracy/variability of the maps makes this type of calculation morecomplex. It becomes a probability based on the accuracy of the map,geometries, and point on the map from which you are calculating and thenextrapolating to a cuboid of area/space.

FIG. 3A shows a detailed block diagram of system architecture forimproving accuracy of a GNSS receiver that has a non-directionalantenna. The system can be implemented in the cloud or using on-premisescapabilities. A cloud implementation can utilize Google Cloud Platform(GCP), Amazon Web Services (AWS), or another platform that providesequivalent capabilities. FIG. 3A shows Google remote procedure calls(gRPC) in the example block diagram. In addition to the gRPCrequest-response model, services could use a message broker for theirasynchronous exchanges. The hosted Amazon MQ for ActiveMQ service, whichis compatible with Apache ActiveMQ, so can be deployed in on-premisesinfrastructure, is utilized in one implementation.

Map curation service (MCS) 384 sends requests for digital terrain model(DTM) and shape files to vendor map processing service (VMPS) 388 viaPNT-A-FAAS/VMPS message queue 386. An example MCS 384 service call islisted next.

service MapCuration {  rpc getFBXPath(TileID) returns (FilePath) { } rpc getMetadataPath(TileID) returns (FilePath) { }  rpcgetVOPBatchesPath(TileID) returns (FilePaths) { } ...

MCS 384 allows upload of new vendor maps and initiates VMPS 388 toconvert them to FBX and associated metadata and store them in FBX metastore 376 for retrieval by forecast engine services (FES) 358. Anexample FES 358 for running a forecast is listed next.

syntax = “proto3”; package spirent.pnta; import “ForecastBatch.proto”;import “google/protobuf/timestamp.proto”; /* FES service to run aforecast. */ service ForecastEngine {  rpc Forecast(ForecastRequest)  returns (ForecastResponse) { } } message ForecastRequest {  uint64job_id = 1;  string orbit_path = 2;  string mesh_path = 3;  stringmetadata_path = 4;  string vop_batch_path = 5;  string output_path = 6;} message ForecastResponse {  google.protobuf.Timestamp processing_time= 1; }

MCS 384 can allow access to the maps that have been uploaded to itthrough gRPC interface, and allow for upload of vendor data pushedthrough VMPS 388. VMPS 388 accepts messages from VMPS message queue 386,a pipeline that can process incoming vendor map data and produce FBXtiles along with the tiles' corresponding metadata, including VOP data.An example PNT-A-FAAS/VMPS message is listed next, as an ActiveMQ JSONmessage as published by the MCS 384 to the VMPS 388.

{ “job_id”: 1, “dataset_s”: “Blacksburg_test”, “src_dtm”:“Blacksburg_sample_1m_Z17N_0_dtm.tif”, “src_bld_shp”:“Blacksburg_sample_17N_0_3d_buildings_automated.shp”, “tile_x_m”: 1000,“tile_y_m”: 1000, “tile_buf_m”: 200, “dtm_trans_i”: −9999,“bld_vop_buf_m_f”: 0.1, “fbx_dec_ratio_f”: 0.1, “vop_level_list”:“1,2,3,4,5,10,20,30”, “publish_all”: False }

An example PNT-A-FAAS/CDNG message is listed next, as an ActiveMQ JSONmessage as published by the FWS 356 to the CDNG 367. The FES 356 willwrite the forecast for each timestep to a binary protocol buffers file.These files will reside in the shared Forecast Processor Store 346.

{ “job_id”: 1, “path”: “forecast_batch0.bin” }

Continuing the description of the block diagram, scheduler curationservice 364 can allow the user to curate which forecasts to run at whichtime. The curation can reside in schedule DB 352 which can be used byscheduler service 354 to push new forecast jobs upon forecast messagequeue PNT-A-FAAS/FWS 355.

An example PNT-A-FAAS/FWS message is listed next, as an ActiveMQ JSONmessage as published by the SS 354 to the FWS 356.

{ “job_id”: 1, “tile_id”: 1, “start”: 1272844800, “end”: 1272844820,“time_resolution_s”: 1, “level”: 1, “elevation_cutoff_d”: 0,“forecasts”: {  “los”: 1,  “azel”: 1,   “pdop”: [ “all”, “gps”,“gps+glonass” ], } }

Scheduler service 354 can also schedule the running of orbit predictionrunner 322 to cache a new 24-hour orbit in advance via PNT-A-FAAS/RINEX302. An example PNT-A-FAAS/RINEX message is listed next, as an ActiveMQJSON message as published by the SS 354 to the RDVS 316.

{ “job_id”: 1, “day_of_year”: 255, “igs_list”: [“BREW”, “BRUX”] }

RINEX downloader and validator service 316 receives RINEX automaticallydownloaded from NASA HTTPS server 318. The downloaded RINEX data is thedaily full constellation published data for the GPS, GLONASS, Galileoand BeiDou constellations. Downloaded RINEX files can be stored in RINEX314 so that they are accessible by orbit prediction service (OPS) 324. Averification process can be required after download. If the file failsto be verified another IGS station's RINEX data could be download andverified in its place. However, if none of the IGS files validate thenthe previous good RINEX file can be used in its place, and this can benoted through logging and event service (LES). Note that IGS stationsprovide continuous tracking using high accuracy receivers and have datatransmission facilities allowing for rapid data transmission to the datacenters.

Logging and event service (LES) 372 is a repository that can record thestate and history of the forecast system. LES can accept, record andreport on both service logging data and real time events. Each servicecan be automatically collected and pushed into LogStash, with atimestamp, which requires that each service be synchronized through NTP.An ELK stack can be utilized for LES, in one implementation.

Orbit processor 312 includes orbit prediction service (OPS) 324 whichpredicts forward locations of satellites and caches data in Redis cache326 and orbit prediction runner (OPR) 322 for caching a new 24-hourorbit in advance. Orbit Prediction Service 324 predicts the orbits forall the satellites within each constellation that has been enabledwithin the scenario run by PosApp. The supported constellations areoutlined in the ‘Constellation’ Enum within the ForecastBatch.proto.Scheduler service 354 can request orbit predictions via PNT-A-FAAS/OPR332. An example PNT-A-FAAS/OPR message 332 is listed next, as anActiveMQ JSON message as published by the SS 354 to the OPR 322.

{ “job_id”: 1, “start”: 1272844800, “end”: 1272844820,“time_resolution_s”: 1 }

The orbit predictions are serialized to orbits store 336, a shared filestore, and the path of the orbit prediction file is returned as part ofthe gRPC message. A full 24-hour prediction for al constellations can beapproximately 300 MB of protocol buffer data and take approximatelytwelve minutes to compute. The cache only stores the file path and henceis not large. OPR 322 is a small ActiveMQ subscriber that listens fornew jobs and then calls the gRPC interface to initiate an orbitprediction, in one implementation of the disclosed technology. OPS 324gRPC/Protobuf format is listed below.

Further continuing the description of the block diagram shown in FIG.3A, forecast worker service (FWS) 356 accepts a message from FWS messagequeue 355 and initiates a forecast and then waits for the success orerror response from forecast engine service (FES) 358. Listed next is ahigh level example implementation of FWS 356 to run a forecast,interacting with the FES 358, OPS 324 and MCS 384.

syntax = “proto3”; package spirent.pnta; import “ForecastBatch.proto”;service ForecastWorker {  rpc Forecast(ForecastWorkerRequest) returns(stream  ForecastWorkerResult) { } } message ForecastWorkerRequest { uint64 job_id = 1;  uint64 tile_id = 2;  GPSTime start = 3;  GPSTimeend = 4;  uint32 time_resolution_s = 5; } message ForecastWorkerResult { string forecast_path = 1; }

Once the FES forecast is complete, FWS 356 can check FWS message queue355 and if messages are queued, FWS 356 can initiate another forecastwith the new message data. FWS 356 has a one-to-one mapping to FES 358,gathering the data required by FES 358 and interacting with the system,allowing FES 358 to be run locally the same way as it would run within acloud environment. Computing all satellites within a constellation forevery second over a 24-hour period only needs to be completed once a dayas the resulting earth-centered, earth-fixed (ECEF) positions can becached and served to any service requesting the data, with normal CDNlatency and delivery time. FES 358 can allow for a simple elevationcut-off since many of the satellites will be NLOS due to the earth butwill now be available to FES 358.

GNSS Forecast engine service (FES) 358 is operable in multiple modes. Inon-demand mode, the data requested by the user has not been forecastedwithin the Spirent Cloud and therefore is not in the CDN 369. This modeis referred to as a CDN cache miss. The request prompts creation of areal-time forecast and the data gets sent from GNSS Forecast engineservice (FES) 358 to the CDN 369, and the CDN 369 routes the datadirectly to the user. This type of request has no upfront storagerequirement in the CDN, and has a higher latency than other modesdescribed herein. The data generated by an on-demand request gets storedin the CDN 369 and in the applicant's cloud for future requests and isavailable until the data's useful life expires.

One way to achieve on-demand forecasting is to add a gRPC call to theCDNG 367, that would be called from the CDN 369 with the on-demandcustomer request. The CDN 369 can first check that the area requested isnot currently covered by the stored data and if not covered, then pushthe request to CDNG 367. On receipt of the request the CDNG 367 couldquery the MCS 384 for the tiles that the request covers. If the areadoes not yet have coverage then a “no-coverage” response is returned tothe CDN 369. If there is coverage for the requested area then an “OK” isreturned to the CDN 369 and the tiles, from, to datetimes are placed ona second “On-Demand” Forecast Message Queue. In turn a ForecastProcessor 344 can be spun up or be waiting in a “stand-by” mode toprocess the request. Once processed, the data can be transferred to theCDN 369 via the CDNG 367 either through the method described above orvia a separate priority gRPC CDN call.

In hybrid mode, the data requested by the user has been forecastedalready in the Spirent cloud and is available in the Spirent clouddatabase, but the forecast data is not available in the CDN 369. Thismode is referred to as CDN cache miss and applicant cloud cache hit. Inhybrid mode, the data will be sent from the Spirent cloud to the CDN 369and the CDN 369 routes the data directly to the user 348. This type ofrequest has no upfront storage requirement in the CDN 369, does have astorage requirement in the applicant's cloud, and will have moderatelatency relative to other modes described herein. The data generated bythis request gets stored in the CDN 369, and is available until thedata's useful life expires.

In cached mode, the data requested by the user is available in the CDN369 and is immediately sent to the user; this mode is referred to as aCDN cache hit. This cached mode has the highest storage requirement andthe lowest latency relative to the modes described herein. In cachedmode, the data gets pushed from the Spirent cloud to the CDN 369. Theservice area and valid time periods are selected by applicant inadvance, based on user demand, SLAs, and cost models. The data is storedon the CDN 369 and is available until the data's useful life expires.

Frequency of data requests, complexity of the requests, forecast time,and latency of the requests are considered for determining what data tocache and which data to provide on demand. Use cases that demand lowlatency are forecasted on demand. Customer density and time of day ofrequests affect the frequency of requests. Requested forecast resolutionand level of complexity of areas affect the complexity of requests.

CDN service 368 includes CDN gateway (CDNG) 367 and an interface forcommunication with devices, users and visualization systems 348. CDNG367 receives messages from the CDNG message queue 366. Each messagerepresents a file to upload to CDN 369. CDNG 367 can read the protocolbuffer file from forecast processor service (FPS) 344 and then call theCDN gRPC function to accept the file stream.

Continuing the description of the block diagram shown in FIG. 3A,console window 372 provides a window into the forecasting system. Theoperator's view of the system can be through a browser-based UI that isdriven by the data retrieved from the scheduler curation service 364,map curation service (MCS) 384 and logging and event service (LES) 372.Descriptions of operator console use case examples follow. (1) Upload ofvendor map data to MCS 384 to be converted to FBX/VOPs, withnotification when conversion is complete. (2) View the full set of 1 Km²tiles that have been converted, via a query to MCS 384 and thenvisualize the tiles as overlays on Open Street Maps, or an equivalentrendering interface. This allows for differentiating between the tilesthat are already part of the forecast schedule and those that are not,and allows the user to select the tiles they wish to add into theforecast scheduler. (3) Change the forecast schedule kick off time,RINEX download time or orbit prediction time, and view the timesrelative to each other. (4) Watch the system as it dynamically schedulesa forecast. When the system dynamically schedules a forecast, thescheduler queues a 1 Km² message on the forecast queue which auto-scalesout the forecast processors to pop a message off the queue and to runthe forecast for the specified tile. As the forecast runs, metrics areflowed back via LES 372. Each time step of forecast is held withinforecast processor store (FPS) 346, to then be retrieved by the CDNG 367to be uploaded to CDN 369. Knowing how full FPS 346 is and, what, andhow much data has been transferred to CDN 369 can help administratorskeep a cap on costs, and ensure the system is functioning as expected.(5) Notifications of stalled or errored processes, such as logging/eventand notification tracing by tile ID. For example, a customer reports aproblem with an area which the user of the system can look up and traceback to a particular tile and then search the logs for that tile ID andtime etc. for diagnostics. (6) Processing of statistics of time, memory,space that each service is taking up, to allow for better utilizationand optimization.

Upon receipt of a query, CDN 369 determines what tiles are needed forthe area, and creates a manifest list with the representation file IDsfrom the included tiles, and sends the manifest list to client, whichdetermines what resolution is needed, and makes file requests for datato CDN file storage. In one example, for 1 km² area with tile size of50×50×1 meters, 400 tiles represent the specified query response. Themanifest list response is a single JSON (or similar file format) with400 tiles of metadata and file IDs. In the low spatial/low dataselection, the file request and file response is 400 files, with 40,000points. For high spatial/low data selection the file request andresponse is for 1600 files, with 1M points. For the high spatial/highdata option, the request and response are for 1600 files, with 1Mpoints. In a second example, for a 1 km route with 50 m width, with tilesize of 50×50×1 meters, 20 tiles represent the specified query response.The manifest list response is a single JSON (or similar file format)with 20 tiles of metadata and file IDs. In the low spatial/low dataselection, the file request and file response is 20 files, with 1,000points. For high spatial/low data selection the file request andresponse is for 80 files, with 50k points. For the high spatial/highdata option, the request and response are for 80 files, with 50k points.In conclusion, GIS DB and CDN file storage architecture yields fast DBsearch and initial API response to client, and keeps DB small and iftiles are not changing can be indexed once. The client determines whatresolutions to request, and may not need to ask twice when going fromarea to path search. New representations can be added in anotherembodiment, without changing the DB. Also client can stream files. Datafiles can be created and stored directly from FES 358 in the GPU, forsome implementations. Computation/aliasing of different resolutions getcomputed efficiently in the GPU.

FIG. 3F, along with the listing below, describe an example forecast filefor a given area (tile) over a time expressed in GPS time i.e. secondssince 1980-01-06T00:00:00+00:00 for a particular type, withForecastBatch 305 describing metadata, ForecastMetadata 321 specifyingthe forecast parameters, message ForecastAzEl 351 describing azimuth andelevation, ForecastLOS setting line of site details, ForecastDOP 371 fordilution of precision, PointForecastLOS 381 for the point forecast lineof sight, and PointForecastDOP 391 for the point forecast line of sight.The forecast file further describes SatelliteAzElForecast 358,SatelliteLOSForecast 387 and DOPResult 398, along with the detailsdescribed in the list below.

message DOPForecast {  GPSTime valid_from = 1;  DOPResult dop = 2; }enum ForecastType {  LOS = 0;  AZ_EL = 1;  PDOP_ALL = 2; } enumConstellation {  GPS = 0;  GALILEO = 1;  GLONASS = 2;  BEIDOU = 3;  WAAS= 4;  EGNOS = 5;  MSAS = 6;  QZSS = 7;  GAGAN = 8;  SDCM = 9; } messageSatellite {  Constellation = 1;  uint32 svid = 2; // Mostly PRN but slotID for GLONASS } message AzimuthElevation {  float azimuth_deg = 1; float elevation_deg = 2; } message Point3DWGS84 {  Point2D position =1;  double height_m = 2; } message Point2D {  double latitude_deg = 1; double longitude_deg = 2; } message Quad2D {  Point2D nw_corner = 1; Point2D ne_corner = 2;  Point2D se_corner = 3;  Point2D sw_corner = 4;} message HeightRange {  double min_m = 1;  double max_m = 2; } messageSatelliteAzEl {  GPSTime valid_from = 1;  AzimuthElevation coordinate =2; } message LOSForecast {  GPSTimeRange los_period = 1; // From/To timewhen signal is LOS } message GPSTimeRange {  GPSTime from = 1;  GPSTimeto = 2; } message GPSTime {  uint64 seconds = 1; }

Forecast engine service (FES) 358 can compute line of sight (LOS),azimuth and elevation and position dilution of precision (PDOP). LOSwill be recorded for every satellite at every VOP. PDOP can be recordedfor every VOP when the there is a change in LOS or every two minutes.Azimuth and elevation can be recorded on a forecast basis for everysatellite when their values change. The term “temporal compression”describes this approach. To make it easier for a client to request data,the height element in their request is assumed to be zero for localground level and then meters above that. The height used by FES 358 iswith reference to the WGS84 ellipsoid. Since local ground can vary frommeter to meter a concept of “level” is required for grouping. The zerolevel is considered as ground and hence FES 358 is required to indicatethe level for which the current dataset is being generated.

Dataflow for forecast engine service 358 can be described in six steps.In step one, vendor map is uploaded to map curation service 384, andconversion to FBX metadata starts. At step two, map administratorselects 1 Km² tiles to add to the forecast scheduler database. At stepthree, forecast schedule, RINEX download and orbit prediction times areschedule to execute. At step four, forecast service auto-scalesprocessor engines to perform GNSS prediction computes. At step five,computed forecast data is moved from cache to CDN, ready foravailability to customers. At step six, system analytics and processingevents are monitored to trace customer feedback and optimize theforecast engine services platform.

FIG. 3B shows a message flow for interactions of forecast worker service(FWS) 356 with other services. To initiate a forecast, scheduler 354pushes messages 301 to forecast message queue 355. FWS 356 pops onemessage 303 and retrieves orbit prediction 313 from OPS 324 asprediction 323, and stores the orbit prediction in FPS 346. Then FWS 356retrieves FBX 335 from MCS 384 as FBX 345, and stores the FBX 347 in FPS346. Then FWS 356 retrieves VOPs 353 from MCS 384 as VOPs 363, andstores them in FPS 346. Then FWS 356 initiates forecast 377 from FES358. FPS 346 reads orbits 378, FBX 387 and VOPs 389 into FES 358. ThenFES 358 runs a forecast for each epoch 397 and stores the forecast ateach epoch 395 in FPS 346. FES 358 also returns the result of each epoch391 to FWS 356 that pushes the results via CDNG message queue for eachepoch 399. Once the FES forecast is complete, FWS 356 can check FWSmessage queue 355 and if messages are queued, FWS 356 can initiateanother forecast with the new message data.

Since all satellites, not just those in view according to PosApp, arepassed to FES 358 for processing, FES 358 can cull satellites below agiven elevation cut off point. A default of zero degrees is utilized inone implementation. Orbit Prediction Service (OPS) 324 predicts theorbits for all the satellites within each constellation that has beenenabled within the scenario run by PosApp. The supported constellationsare outlined in the ‘Constellation’ Enum within the ForecastBatch.proto,which is described in the Forecast Assured Navigation (FAN) APIs sectionbelow. Times are in GPS time i.e. seconds since1980-01-6T00:00:00+00:00. OPS 324 gRPC/Protobuf format is listed next.

syntax = “proto3”; package spirent.pnta; import “ForecastBatch.proto”;service OrbitPredictionService {  rpc Predict(OrbitPredictionRequest)  returns (OrbitPredictionResult) { } } message OrbitPredictionRequest { uint64 job_id = 1; // Job ID trace  GPSTime start = 2;  GPSTime end =3;  uint32 resolution_s = 4; // If not defined then assumed to be 1second } message OrbitPredictionResult {  uint64 job_id = 1; // Job IDtrace  string path = 2; // Path to prediction file, relative to mount }message OrbitPrediction {  GPSTime valid_from = 1;  GPSTime valid_to =2;  uint32 time_resolution_s = 3;  repeated TimedSatellitePositionseries = 4; } message TimedSatellitePosition {  GPSTime timestamp = 1;// Time of applicability of position  repeated SatellitePositionsatellite = 2; } message ECEFPosition {  double x = 1; // Metres  doubley = 2; // Metres  double z = 3; // Metres } message SatellitePosition { Satellite = 1; // Constellation and SVID  ECEFPosition position = 2; }

Scheduler curation service 364 allows a user to set up jobs to run everyday and allows those jobs to accept templated message parameters, sothat the services are call with the correct arguments. Forecasts aretile based and are required to run for a given prediction duration froma start date-time. At least once daily, scheduler curation service 364runs for a given duration from a start date-time. OPR 322 subscriberlistens for new jobs and then calls the gRPC interface to initiate anorbit prediction. Orbit prediction service (OPS) 324 accesses downloadedRINEX files stored in RINEX 314. If the requested forecast is not in theorbit cache, then OPS 324 retrieves the closest RINEX files for thegiven date, and predicts orbits, and send the orbit prediction to OPR322. OPS 324 also sends the prediction to orbit cache for retrieval byother services, in one implementation.

APIs for Forecast Assured Navigation (FAN)

APIs for requesting and retrieving forecast points and forecast areasare disclosed for retrieving a forecast for a set of points at groundlevel, for 2D, and at any height relative to the ground for 3D, via theWGS84 standard U.S. DoD global reference system for geospatialinformation. GNSS Forecast arrays include DOP for the satellites.

Forecast ingress API defines the interface protocol between CDNG 367 andCDN 369. The ForecastBatch.proto defines the data format of theForecastBatch datafiles which are uploaded from FES 358 to CDN 369 viaCDNG 367. The proto protocol buffer utilizes a language-neutral,platform-neutral, extensible mechanism for serializing the structureddata. The customer facing API endpoints will be served by CDN 369.Communication between CDN 369 and CDNG 367 is based on the gRPCframework, in one implementation with CDN providing a gRPC response tothe user/device 348. A user/device 348 may opt to formulate datarequests to CDN 369 using HTTP requests, in a different embodiment. AForecastBatch.proto datafile is a single block of output by FES 358. Inone example, this represents an area of 100×100 m2. Map tiles correspondto a 1.2×1.2 km2 area, in one implementation. The size dimensions can bedifferent in different implementations. ForecastBatch.proto parametersare described next.

<containing message>.<qualified Datatype parameter name> (Message orother) Units Notes ForecastBatch (Overarching message) — TheForecastBatch message is the highest-level message that encapsulates allthe subsequent messages. ForecastBatch.forecastbatch_metadata Metadata —The Metadata message contains other messages explained in this sectionand applies to the specific forecast batch. ForecastBatch.per_pointForecastPerPoint — This is the container message for the forecastparameters (DOP and LOS only) applying to each observation point.ForecastBatch.per_batch ForecastPerBatch — This is the container messagefor the azimuth/elevation forecast parameters applying to the wholeForecastBatch corresponding area (100 m × 100 m in one use case).Metadata.forecast_validity_period GPSTimeRange — Start and End ofvalidity period of forecast batch in GPS time. Metadata.area Quad2D —This area comprises four points that define the horizontal (Lat/Lon)coordinates of the lower and upper faces of the bounding box.Metadata.height_agl double meters The AGL height of the forecast batchis the height above ground level. Note that ground level is always AGL =0 m. AGL can be a positive or negative number. Metadata.height_agl_rangeHeightAGLRange — See explanation for HeightAGLRange message.Metadata.height_wgs84_range HeightWGS84Range — See explanation forHeightWGS84Range message. Metadata.point_grid_resolution double metersThe grid resolution is a positive number, stating the distance betweenadjacent prediction points. Default value is 1 m. Metadata.forecast_typeForecastType — See explanation for ForecastType message.ForecastPerPoint.point_forecast PointForecast — See explanation forPointForecast message. ForecastPerBatch.satellite_az_el_forecastSatelliteAzElForecast — See explanation for SatelliteAzElForecastmessage. PointForecast.point Point3DWGS84 — The 3D coordinates of theobservation point in WGS84. PointForecast.los SatelliteLOSForecast — Thecontainer for LOS forecast information for this observation point, seeexplanation of SatelliteLOSForecast message for more details.PointForecast.dop DOPForecast — The container for PDOP forecastinformation for this observation point, see explanation of DOPForecastenumeration list for more details. Only PDOP using all availablesatellites and constellations is provided currently, future releases mayinclude other DOP types, e.g. GDOP, or different constellationcombinations e.g. GPS + GLONASS only. SatelliteAzElForecast.satelliteSatellite — This parameter contains all the essential information toidentify a satellite in the forecast (i.e., SVID, see svid explanationbelow) and constellation). SatelliteAzElForecast.coordinatesSatelliteAzEl — This contains the azimuth/elevation (rounded to thenearest degree) for each satellite in the forecast batch, noting thatthe Az/El do not change between observation points within the forecastbatch, as the size of the corresponding area (100 m × 100 m) has anegligible effect on the satellite azimuth and elevation observed byindividual forecast points. GPSTimeRange.from GPSTime — GPSTimeRangeprovides a GPSTimeRange.to GPSTime — range of time values in GPS time.It can be used to define data validity periods, or the applicable periodof a user's request and the corresponding manifest file response by theCDN. It should be noted that GPS Time is different to UTC time by thenumber of leap seconds since the start of GPS Time (midnight of Jan.6^(th), 1980). At the time of writing, UTC Time lags 18 seconds relativeto GPS Time. Quad2D.nw_corner Point2D — These four points define theQuad2D.ne_corner Point2D — horizontal coordinates of the ofQuad2D.sw_corner Point2D — the lower and upper surfaces ofQuad2D.sw_corner Point2D — the cuboid. HeightAGLRange.min_height_agldouble meters This is the minimum AGL height of the bounding box.HeightAGLRange.max_height_agl double meters This is the maximum AGLheight of the bounding box. HeightWGS84Range.min_height_wgs84 doublemeters This is the minimum WGS84 height of the bounding box.HeightWGS84Range.max_height_wgs84 double meters This is the maximumWGS84 height of the bounding box. ForecastType enum unitless Enumerationof values: “DOP”, “LOS”, “AZ_EL”. This element is also provided tofuture-proof the ForecastBatch proto file. This is the type of theForecast data provided in each ForecastBatch “DOP” provides informationfor “LOS” and “AZ_EL” too, in some implementations..Point3DWGS84.position Point2D — Point3DWGS84 provides thePoint3DWGS84.height_wgs84 double meters 3D coordinates of a singleForecast observation point in WGS84. SatelliteLOSForecast.satelliteSatellite — The SatelliteLOSForecast.measurement LOSForecast —SatelliteLOSForecast message provides information about when a satelliteis LOS as a series of repeated periods (whose start and end are in GPStime). It follows that a given satellite is NLOS in- between thoseperiods. DOPForecast.valid_from GPSTime — This is the GPS time that thePDOP forecast is valid from. Note that the PDOP has no expiry time, butit is revoked instead by a new PDOP forecast. DOPForecast.dop_resultDOPResult — See explanation for DOPResult message.Satellite.constellation Constellation — See explanation forConstellation enumeration list. Satellite.svid uint32 unitless PRN forall constellations except for GLONASS where it represents thesatellite's slot number. SatelliteAzEl.valid_from GPSTime — The start ofvalidity of the satellite azimuth/elevation of the forecast batch in GPStime. SatelliteAzEl.coordinate AzimuthElevation — The satelliteazimuth/elevation of the forecast batch in GPS time.LOSForecast.los_forecast_validity GPSTimeRange — This is a single GPStime range that shows when a satellite is visible. GPSTime.secondsuint64 seconds GPS Time is the number of seconds since the start of GPSTime (Jan. 6^(th), 1980). Example: a value of 1,857,945,600 correspondsto midnight of Sunday, 21 Nov. 2038. Point2D.latitude double degreesLatitude in decimal degrees relative to WGS84. Point2D.longitude doubledegrees Longitude in decimal degrees relative to WGS84. Constellationenum — Enumeration of GNSS/SBAS constellations: “GPS”; “GALILEO”;“GLONASS”; “BEIDOU”; “WAAS”; “EGNOS”; “MSAS”; “QZSS”; “GAGAN”; and“SDCM”. DOPResult enum — Enumeration of PDOP value classes: “IDEAL”[0-1); “EXCELLENT” [1-2); “GOOD” [2-5); “MODERATE” [5-10); “FAIR”[10-20); “POOR” [20+); “NO_DOP”, e.g., “IDEAL” corresponds to a PDOPfrom 0 to 1 (not inclusive). A “NO_DOP” value means that less than foursatellites were available at the time of PDOP calculation. Note that atthe time of writing, PDOP is calculated when there is a change in theset of LOS satellites, due satellite(s) rising/setting, or two minutesafter the last PDOP calculation, even if there have been no changes inthe LOS set of satellites since the last PDOP calculation.AzimuthElevation.azimuth float degrees Azimuth of a satellite w.r.t.ForecastBatch centroid local horizon (North at 0° and positiveanti-clockwise). Range from 0° (inclusive) to 360° (exclusive), roundedto the nearest 1°. AzimuthElevation.elevation float degrees Elevation ofa satellite w.r.t. ForecastBatch centroid local horizon. Range from −90°(inclusive) to +90° (inclusive), rounded to the nearest 1°, noting thatat the time of writing the FES will exclude from the calculations anysatellites with negative elevation.

Device API defines the interface protocol between a user/devicevisualization app and CDN 369. The device API is realized by the deviceAPI file which defines the serialized binary format of the user/devicerequests to CDN 369, and the subsequent CDN gRPC response. TheDeviceAPI.proto file definitions are wrapped into anavmatix.gnss_forecast.device_api package. Device API is listed next.

service Device {  rpcGetManifestBy3DAGLPolygon(GetManifestBy3DAGLPolygonRequest) returns(Manifest) { }  rpcGetManifestBy3DWGS84Polygon(GetManifestBy3DWGS84PolygonRequest) returns(Manifest) { }  rpcGetManifestBy3DAGLPath(GetManifestBy3DAGLPathRequest) returns (Manifest){ }  rpc GetManifestBy3DWGS84Path(GetManifestBy3DWGS84PathRequest)returns (Manifest) { } } service Auth {  rpc Auth(AuthRequest) returns(AuthResponse) { } } message GetManifestBy3DAGLPolygonRequest { spirent.pnta.GPSTimeRange request_time_range = 1; // Start and End timeof request in GPS time  double min_height_agl = 2;  doublemax_height_agl = 3;  repeated Point2D points = 4;  repeatedspirent.pnta.ForecastType types = 5; // Types of forecast requested }message GetManifestBy3DWGS84PolygonRequest {  spirent.pnta.GPSTimeRangerequest_time_range = 1; // Start and End time of request in GPS time double min_height_wgs84 = 2;  double max_height_wgs84 = 3;  repeatedPoint2D points = 4;  repeated spirent.pnta.ForecastType types = 5; //Types of forecast requested } message GetManifestBy3DAGLPathRequest { spirent.pnta.GPSTimeRange request_time_range = 1; // Start and End timeof request in GPS time  repeated Point3DAGL points = 2;  repeatedspirent.pnta.ForecastType types = 3; // Types of forecast requested }message GetManifestBy3DWGS84PathRequest {  spirent.pnta.GPSTimeRangerequest_time_range = 1; // Start and End time of request in GPS time repeated spirent.pnta.Point3DWGS84 points = 2;  repeatedspirent.pnta.ForecastType types = 3; // Types of forecast requested }message Manifest {  repeated ManifestItem items = 1; } messageManifestItem {  string url = 1;  spirent.pnta.ForecastMetadatamanifest_item_metadata = 2;  Compression = 3;  int64 file_size = 4; //In bytes  string file_hash = 5; // SHA-256 as hex } message Point2D { double latitude = 1;  double longitude = 2; } message Point3DAGL { Point2D position = 1;  double height_agl = 2; } message AuthRequest { string api_key = 1; } message AuthResponse {  string auth_token = 1; }enum Compression {  NONE = 0; // No compression }

DeviceAPI.proto imports the message definitions from theForecastBatch.proto to avoid duplicate messages/datatypes declarationsand to facilitate consistency and maintenance of these files, so acustomer needs to be provided both DeviceAPI.proto andForecastBatch.proto files. DeviceAPI.proto file parameters are describednext.

<containing message>.<qualified Datatype parameter name> (Message orother) Units Notes Device (service) — The service encapsulates a numberof RPC methods to accommodate customer path/ polygon and WGS84/AGLheight requests as explained in this table. GetManifestBy3DAGLPolygon(method) — Input argument: GetManifestBy3DAGLPolygonRequest Response:Manifest GetManifestBy3DWGS84Polygon (method) — Input argument:GetManifestBy3DWGS84PolygonRequestet Response: ManifestGetManifestBy3DAGLPath (method) — Input argument:GetManifestBy3DAGLPathRequest Response: ManifestGetManifestBy3DWGS84Path (method) — Input argument:GetManifestBy3DWGS84PathRequest Response: Manifest Auth (service) —Input argument: AuthRequest Response: AuthResponse This is theauthorisation process request - a user sends an API key (provided bySpirent) to the CDN, and if successful, the CDN returns an authorisationtoken with expiry date. GetManifestBy3DAGLPolygonRe-spirent.pnta.GPSTimeRange — Start and end time of user requestquest.request_time_range (quadrilateral polygon with AGL height) in GPStime. GetManifestBy3DAGLPolygonRequest.min_height_agl meters Minimum AGLheight of user-requested quadrilateral polygon.GetManifestBy3DAGLPolygonRequest.max_height_agl meters Maximum AGLheight of user-requested quadrilateral polygon.GetManifestBy3DAGLPolygonRequest.points Point2D — A set of at least four2D points (Lat/Lon) of user-requested polygon in WGS84. A closed polygonis expected (i.e., by repeating the first entered point).GetManifestBy3DWGS84PolygonRe- spirent.pnta.GPSTimeRange — Start and endtime of user request quest.request_time_range (quadrilateral polygonwith WGS84 height) in GPS time. GetManifestBy3DWGS84PolygonRe- metersMinimum WGS84 height of user-requested quest.min_height_aglquadrilateral polygon. GetManifestBy3DWGS84PolygonRe- meters MaximumWGS84 height of user-requested quest.max_height_agl quadrilateralpolygon. GetManifestBy3DWGS84PolygonRequest.points Point2D — A set of atleast four 2D points (Lat/Lon) of user-requested polygon in WGS84. Aclosed polygon is expected (i.e., by repeating the first entered point).GetManifestBy3DAGLPathRequest.request_time_rangespirent.pnta.GPSTimeRange — Start and end time of user request (pathwith AGL height) in GPS time. GetManifestBy3DAGLPathRequest.pointsPoint3DAGL — Set of repeated 3D points (Lat/Lon in WGS84, height in AGL)of user-requested path. At least one point is expected in the userrequest. GetManifestBy3DWGS84PathRequest.request_time_rangespirent.pnta.GPSTimeRange — Start and end time of user request (pathwith WGS84 height) in GPS time. GetManifestBy3DWGS84PathRequest.pointsspirent.pnta.Point3DWGS84 — Set of repeated 2D points (Lat/Lon) of user-requested path in WGS84. At least one points is expected in the userrequest. Manifest.items ManifestItem — See explanation for ManifestItemdatatype. ManifestItem.url string — The URL link containing the manifestitem (Forecast Batch) underlying data stored in the CDN.ManifestItem.manifest_item_metadata spirent.pnta.Metadata —ManifestItem.compression Compression See explanation for Compressionenumeration list. ManifestItem.file_size int64 bytes Manifest item filesize (accessed via provided ManifestItem.url link)ManifestItem.file_hash string — The ForecastBatch datafile hash, SHA-256as hex. Point2D.latitude double degrees Latitude in decimal degreesrelative to WGS84. Point2D.longitude double degrees Longitude in decimaldegrees relative to WGS84. Point3DAGL.position Point2D Position inLat/Lon in WGS84 of a user-requested path. See explanations for Point2Dand GetManifestBy3DAGLPathRequest messages. Point3DAGL.height_agl doublemeters AGL height of a user-requested path. See explanation forGetManifestBy3DAGLPathRequest message. AuthRequest.api_key string Theauthorisation user request API key, provided to the user by Spirent, orgenerated automatically by the user for a set of devices.AuthResponse.auth_token string The returned authorisation token to theuser by the CDN to enable (time-limited) access. Compression enumontains compression formats for the underlying manifest items, so theuser knows which decompression method to select.

FIG. 3C shows an example bounding box. To facilitate the selection ofForecast Batch data that correspond to a particular userpoint/trajectory/area, each ForecastBatch.proto contains metadatainformation about the coordinates of the bounding box corner points.Each bounding box encapsulates the forecast observation points alongwith their respective validity volumes, and can be used to designspatial 3D queries to identify which ForecastBatch.proto data need to bereturned to a user/device 348. The parameters in the ForecastBatch.protofor the definition of the eight corner points of a bounding box are thefollowing: (a) Quad2D, (b) HeightAGLRange, for height representation inAGL, comprising a minimum and maximum AGL height in meters; and (c)HeightWGS84Range, for height representation in WGS84, comprising aminimum and maximum WGS84 height in meters. The top face 333 and thebottom face 343 of the bounding box have the same horizontal latitudeand longitude coordinates, defined by Quad2D, and different AGL or WGS84heights. The bounding box corner points' heights are provided in bothAGL and WGS84, as different users may wish to request forecast data byentering heights relative to AGL or WGS84, depending on their specificapplication requirements. The bounding box corner points coordinates(both in AGL height or WGS84 height), using ForecastBatch.proto fileparameters, are listed below.

Point (Latitude, Longitude) AGL height WGS84 height A Quad2D.sw_cornerHeightAGLRange.min_height_agl HeightWGS84Range.min_height_wgs84 BQuad2D.se_corner HeightAGLRange.min_height_aglHeightWGS84Range.min_height_wgs84 C Quad2D.ne_cornerHeightAGLRange.min_height_agl HeightWGS84Range.min_height_wgs84 DQuad2D.nw_corner HeightAGLRange.min_height_aglHeightWGS84Range.min_height_wgs84 A₁ Quad2D.sw_cornerHeightAGLRange.max_height_agl HeightWGS84Range.max_height_wgs84 B₁Quad2D.se_corner HeightAGLRange.max_height_aglHeightWGS84Range.max_height_wgs84 C₁ Quad2D.ne_cornerHeightAGLRange.max_height_agl HeightWGS84Range.max_height_wgs84 D₁Quad2D.nw_corner HeightAGLRange.max_height_aglHeightWGS84Range.max_height_wgs84

A customer can request a forecast with a single point or a path, in oneuse case. User/device 348 can request forecast data for a single pointor path over a given time period in GPS time, viaGetManifestBy3DAGLPathRequest and GetManifestBy3DWGS84PathRequest gRPCcalls. The customer can request the forecast data by inputting the path3D coordinates using WGS84 latitude and longitude, with the heightrelative to WGS84 or AGL. DeviceAPI.proto includes two path requestmethods to accommodate both height specification options.

A customer can request a polygon, in another use case. User/device 348can request forecast data within the volume of a parallelepiped for agiven time-period, in GPS time via GetManifestBy3DAGLPolygonRequest andGetManifestBy3DWGS84PolygonRequest gRPC calls. DeviceAPI.proto includestwo path request methods to accommodate both WGS84 and AGL heightspecification options for requesting a polygon. The user enters thehorizontal latitude and longitude coordinates in WGS84 to define apolygon, and the min/max height, either in AGL or WGS84, for therequested forecast data. The API expects a closed polygon, so theuser/device repeats the first entered point in the request. A userenters the horizontal coordinates of four points along with min/maxheights, then the system combines this information to define the volumeof an eight-point parallelepiped and return the forecast data withinthis volume. The upper corner points (A₁, B₁, C₁, D₁) represent the userpolygon at the user-entered maximum height, and the lower corner points(A, B, C, D) represent the user polygon at the user-entered minimumheight.

Due to the large data sizes for GNSS Forecasts, multiple resolutions areutilized. Three dimensions of data resolution are spatial for cuboidsize, temporal for forecast period, and data detail/depth for DOP, SVLOS/NLOS, SV geometry, and number of constellations. In the disclosedGIS database, each DB entry contains metadata about the file, includingcoordinates, valid period and representation file IDs. Eachrepresentation file contains N number of forecasted cuboids/points withthe data at high or low resolution. Representation files are storeddirectly from the GPU to CDN. In one example, for tile size of 50×50×1meters, representation files include the following representations. (1).For informational representation, each file contains 50×50×1 meter.Cuboid is 50×50×1 with some info about the variability in the tile. (2)For low spatial resolution & low data resolution, each file contains50×50×1 meter. Cuboid size 5×5×1 (aliased by GPU). DOP/SV info only. (3)For high spatial & low data resolution, each file contains 25×25×1meter. Cuboid size 1×1×1. DOP/SV info only. (4) For high spatial & highdata resolution, each file contains 25×25×1 meter. Cuboid size 1×1×1.DOP/SV/Geometry info. Additional representations can include multipathresolution level, and separating GPS from GNSS constellations, in otherimplementations.

The CDN stores collections of cuboids within tiles. The metadata of thetile can be stored in the DB while the data can be stored in one or morerepresentation files containing multiple versions of the data for thatarea and time. Upon a user/device request for an area and time the CDNcan search for the appropriate tiles in the database. A manifest filecan be created and sent containing metadata for the area and time andthe representation file(s) location(s) for all of the appropriatetile(s). The device/user can then read the manifest file and downloadsome or all representation data files from the CDN cache. Files may becompressed and may be decompressed for processing by the CDN ordevice/user.

Visualizations of GNSS Forecasts can be represented in tiles, withdifferent shadings representing areas with clear sailing, partiallyimpacted areas, and areas with blockages, respectively, in one exampleimplementation. Tiles can be stacked in multiple layers to includeheight and time. Users can query an area, and receive manifest filesthat include tile pointers with information about the variability in thetile.

FIG. 3D shows a sketch of a stack of shaded tiles, and shows an example1 Km² output buffer of FES 358, with the Azimuth/Elevation 357, LOS 377and PDOP 379 data for each 100×100 m area. Each FES output file cancontain a tiles worth of data for a 24-hour period, in oneimplementation with each LOS file estimated to be in the range of 50-60MB in size. For a 1 Km² area at 1 m resolution for 24 hours the size isapproximately 5 GB. For an example, scaled up to 6000 Km² the size isapproximately 30 TB for a given 24 hour period and over 7 days, with 37levels, the data size is approximately 7.8 PB. The size of forecast datafiles motivates the need for compression.

Compression of GNSS Forecast data can be segmented into three types ofcompression: spatial, temporal and mathematic compression. Disclosedcompression-related parameters are described for the DeviceAPI.protofiles above.

Spatial compression analyzes each cuboid or point in the GNSS pointcloud based on location. Spatial compression analyzes the similaritiesand differences of the data of one cuboid to the cuboids around it, in away that is similar to the way a video pixel is compressed based on thepixels around it (left, right, above, below, etc.). If the data in theadjacent or nearby cuboids is the same or similar, compression can beapplied. A lossless compression searches for identical cuboids, encodesone fully with all the data, and then cuboids with the same data pointto the reference cuboid with the full information. A lossy compressionmethod applies a threshold to look for cuboids that have smalldifferences relative to the reference cuboid, determines that thedifference is not inconsequential to the user and points those cuboidsto the reference cuboid. Another compression method changes the size ofthe cuboid to cover a larger area instead of using pointers. As anexample, if a 1 meter cubed cuboid (1 m×1 m×1 m) has the same or similardata as three adjacent cuboids, the system can change the size of thecuboid to cover the total area with one data set. That is, the size ofthe cuboid would be changed to 3 m×1 m×1 m).

Temporal compression analyzes each cuboid or point in the GNSS pointcloud based on location and time. Temporal compression analyzes thesimilarities and differences of the data of one (or more) cuboid(s) overtime. If the data of one or more cuboids are the same or similar overmultiple time periods, compression can be applied. Either lossless orlossy compression can be used. A lossless compression, for instancebased on run length encoding, looks for cuboids that do not change overmultiple time periods, encodes one fully with all the data and thencuboids with the same data point to the reference cuboid with the fullinformation. A lossy compression method, with banding or quantization,applies a threshold to look for cuboids that have small differences tothe reference cuboid, determines that the difference is notinconsequential to the user and point those cuboids to the referencecuboid's time period and data. Another compression method changes thevalid time period of a cuboid to cover a longer time instead of usingpointers. As an example, if a 1 meter cubed cuboid (1 m×1 m×1 m) validfor one second has the same or similar data to the same cuboids over thenext 29 seconds, a single cuboid and data can be stored with a 29 secondvalid period instead of 1 second valid period.

Spatial and temporal compressions can be combined to compress based onthe similarities and differences of cuboids over the forecasted area andtime. For example, a cuboid in one location and time can be the same asanother cuboid in a different location and time. These cuboids can becompressed as a reference to an original cuboid and how it moves overtime, in a way similar to methods used in video compression.

Mathematic compression looks at patterns in the data with or withoutreference to temporal or spatial information. Similar patterns aremathematically compressed. Hashes compress similar data mathematically.For instance if there is a common combination of data such as satellitepositions, visibility, DOP values, etc., these get hashed andcompressed.

GNSS Forecast data flows from a cloud-based engine to GNSS CDN to awireless carrier in one scenario. The data flows from GNSS CDN to USS/AVservice in another scenario. In one example for data size analysis, GNSSForecast engine service (FES) 358 generates approximately 4.1 PB rawdata and transfers via Serial AT Attachment (SATA) at 400 Gbps toforecast storage approximately 1 PB primary data, transferrable to GNSSCDN 369, resulting in approximately 100 TB compressed data in a week.For a day's worth of data, at 400 Gbps that is 44.4 minutes. At 10 Gbpsthat is 29.6 hours. GNSS CDN 369 transfers data to wireless carrier atapproximately 1 MB per KM, with bursts along the route. Similarly, GNSSCDN 369 transfers data to USS/AV service at approximately 10 MBcompressed data per request. At one Gbps, transmission time per requestis approximately one second.

API Use Cases

For various use cases, forecast engine service APIs cover multiplescenarios described next. In one scenario, a user does not know theirroute and they are not moving. In this situation, a large area getssearched. For aviation, the area may be 1 KM to 10 KM, at one or morealtitudes, in one example. For vehicles, the area may be 5 KM to 30 KM,at a single altitude, and can be larger. A timeframe gets considered,such as 0 h-48 h into the future, a single start time, or a 2-hour timeperiod. Forecast predictions include DOP and #sat (LOS, NLOS, NA) fordetermining the “good” areas. In some designs, the ability to zoom inand out is included as a service via the user interface. The goal is todetermine an improved route based on navigation performance, leading toits use in a second scenario.

In a second scenario, the user knows their route and wants to know withhigh-fidelity, whether the route is assured, for route planning. Foraviation, the user additionally needs to learn the positioning error ofa UAS flight path/volume for planning or surveillance. Once the route isidentified, the customer can download the complete route or can streamthe route, for the identified time period. DOP and #sat (LOS, NLOS, NA)can be utilized to determine whether the route is good, for a navigationsystem, planning, and for USS. If the route is deemed to be not “goodenough”, the forecast engine can return to the approach describedearlier for scenario one.

In a third scenario, the user does not know their route and they aremoving. In this scenario, Forecast Engine Service 358 searches a smallarea ahead, in which the customer may navigate. In one example, lessthan 500 m, high fidelity, with no zoom in or out. The distance ahead tobe considered describes the refresh rate, as does the frequency of theAPI request. For vehicles, the current road ahead and any roads thatcould be selected are also taken into account, in one example. Foraviation, the user additionally needs a bubble around the UAS, and theuse of DOP and #sat (LOS, NLOS, NA) to determine where the user can goand maintain their ODD, no-go areas, and fail-safe.

FIG. 3E illustrates an Amazon Web Services (AWS) Cloud implementation ofthe architecture, with hosted message broker service Amazon (AWS) MQ forActiveMQ service deployed. FIG. 3E shows a detailed block diagram of thecomponents and protocols of system architecture 300, chosen for the AWScloud to reflect the architecture choices described relative to FIG. 3A.By default, the services are deployed in Fargate mode (AWS serverlesscompute engine), except for services dependent on GPU resources deployedon instance containers of type EC2/GPU.

The service APIs are described in the interface design language (IDL)Protocol Buffer Definition Language, and therefore communication isperformed by the RPC gRPC protocol using the HTPP/2 transport. Inaddition to gRPC's request-response model, services will use a messagebroker for their asynchronous exchanges. The solution chosen is thehosted Amazon MQ for ActiveMQ service, which is compatible with ApacheActiveMQ, which can be deployed in on-premises infrastructure. Incomingrequests in HTTP/2 are translated to HTTP/1.1 and therefore cannot reachgRPC endpoints, because support for gRPC is limited in AWS services. Inone example, AWS Application Load Balancers do not fully support HTTP/2,but this limitation does not exist for inter-service calls inside thevirtual private cloud (VPC). Cluster level and load balancer level gRPChealth checks are supported.

Map curation service (MCS) 3084 sends requests for digital terrain model(DTM) and shape files to vendor map processing service (VMPS) 3088 viaAWS MQ 3086. MCS 3084 allows upload of new vendor maps and initiatesVMPS 3088 to convert them to FBX and associated metadata and store themin FBX meta store 3076 for retrieval by forecast engine services (FES)3058. MCS 3084 can allow access to the maps that have been uploaded toit through gRPC interface, and allow for upload of vendor data pushedthrough VMPS 3088. VMPS 3088 accepts messages from VMPS message queue3086, a pipeline that can process incoming vendor map data and produceFBX tiles along with the tiles' corresponding metadata, including VOPdata.

Continuing the description of the block diagram, scheduler curationservice 3064 can allow the user to curate which forecasts to run atwhich time. The curation can reside in PostgreSQL scheduler DB 3052which can be used by scheduler service (SS) 3054 to push new forecastjobs via AWS MQ forecast request 3055.

An example ActiveMQ JSON message as published by the SS 3054 to FWS 3056is listed next.

{ “job_id”: 1, “tile_id”: 1, “start”: 1272844800, “end”: 1272844820,“time_resolution_s”: 1, “level”: 1, “elevation_cutoff_d”: 0,“forecasts”: {  “los”: 1,  “azel”: 1,   “pdop”: [ “all”, “gps”,“gps+glonass” ], } }

Scheduler service 3054 can also schedule the running of orbit predictionrunner 3022 to cache a new 24-hour orbit in advance via AWS MQ 3032.

RINEX download validator service 3016 receives RINEX automaticallydownloaded from NASA website 3018. The downloaded RINEX data is thedaily full constellation published data for the GPS, GLONASS, Galileoand BeiDou constellations. Downloaded RINEX files can be stored in EFSvolume RINEX data 3014 so that they are accessible by orbit predictionservice (OPS) 3024. A verification process can be required afterdownload. If the file fails to be verified another IGS station's RINEXdata could be download and verified in its place. Note that IGS stationsprovide continuous tracking using high accuracy receivers and have datatransmission facilities allowing for rapid data transmission to the datacenters. OPS 3024 and CDNG service 3067 utilize in-memory caches. TheAWS Cloud implementation can use either Memcached or Redis, which areboth available as managed services in AWS.

Orbit prediction service (OPS) 3024 predicts forward locations ofsatellites and caches data in Redis orbit data cache 3026 and orbitprediction runner (OPR) 3022 caches a new 24-hour orbit in advance.Scheduler service 3054 can request orbit predictions via AWS MQ 3032. Anexample OPR message 3032 is listed next, as an ActiveMQ JSON message aspublished by the SS 3054 to the OPR 3022.

{ “job_id”: 1, “start”: 1272844800, “end”: 1272844820,“time_resolution_s”: 1 }

A full 24-hour prediction for all constellations can be approximately300 MB of protocol buffer data and take approximately twelve minutes tocompute. OPR 3022 is a small ActiveMQ subscriber that listens for newjobs and then calls the gRPC interface to initiate an orbit prediction,in one implementation of the disclosed technology. OPS 3024gRPC/protobufs format is listed below.

Further continuing the description of the AWS cloud implementation shownin FIG. 3E, forecast worker service (FWS) 3056 accepts a message fromAWS MQ forecast request message queue 3055, initiates a forecast andthen waits for the success or error response from forecast engineservice (FES) 3058. Once the FES forecast is complete, FWS 3056 cancheck FWS message queue 3055 and if messages are queued, FWS 3056 caninitiate another forecast with the new message data. FWS 3056 has aone-to-one mapping to FES 3058, gathering the data required by FES 3058and interacting with the system.

Apache ActiveMQ supports large messages with type Blob, which containsmetadata, including the message's location; large messages will beaccessible via the EFS file system. CDN gateway (CDNG) 3067 receivesmessages via AWS MQ forecast blob data from forecast processor 3044.Each message represents a file to upload to CDN 369. CDNG 3067 can readthe protocol buffer file from forecast processor service (FPS) 3044 andthen call the CDN gRPC function to accept the file stream. AWSCloudWatch provides numerous metrics via Amazon MQ which is wellintegrated. The implementation of FES 3058 and FWS 3056 autoscalinggroups is based on those metrics, such as a queue or topic size.

Forecast engine service (FES) 3058 can compute line of sight (LOS),azimuth and elevation and position dilution of precision (PDOP). LOSwill be recorded for every satellite at every VOP. PDOP can be recordedfor every VOP when the there is a change in LOS or every two minutes.Azimuth and elevation can be recorded on a forecast basis for everysatellite when their values change. The term “temporal compression”describes this approach. To make it easier for a client to request data,the height element in their request is assumed to be zero for localground level and then meters above that. The height used by FES 3058 iswith reference to the WGS84 ellipsoid. Since local ground can vary frommeter to meter a concept of “level” is required for grouping. The zerolevel is considered as ground and hence FES 3058 is required to indicatethe level for which the current dataset is being generated.

AWS EFS storage service provides the storage volumes used by FES 3058,MCS 3084, Forecast Processor 3044, CDN Gateway service 3067 and theRINEX Download service 3016. AW EFS storage service allows file systemsto be mounted in multiple regions, accessible to multiple instances at atime, with high IOPS and low latency. This approach to storage providesa common mechanism for the AWS cloud and on-premises infrastructures.

To monitor PNT-A services, the infrastructure will use the ELK stack,which refers to three open-source projects: Elasticsearch, Logstash, andKibana. Often referred to as Elasticsearch, the ELK stack aggregatesservice logs, investigates logs, and creates dashboards and alerts forservice and infrastructure monitoring. The AWS platform provides ELKstack through the AWS Elasticsearch Managed Service.

For metrics and custom service metrics aggregation and monitoring, theinfrastructure uses the open-source Prometheus project. AWS providesPrometheus Metrics as a managed service. Prometheus collects the servicecontainers' metrics in its time-series database and makes them availableto the Grafana visualization application, with Prometheus and Grafanacurrently in use for Jenkins CI reporting.

FIG. 4A illustrates a 2D map orbit with GNSS satellites, and an exampleerror calculation for a 2D point. In a typical situation in an urbanarea, the reception of each satellite signal depends on the position ofthe receiver and the satellite with respect to each other. Satellites S2444 and S3 446 are in line of sight (LOS) with 2D point 465. SatelliteS4 458 is blocked and satellite S1 452 has non-line-of-sight (NLOS);that is, S1 452 utilizes multipath signals that reach the receivingantenna by two or more paths. Causes of multipath include atmosphericducting, ionospheric reflection and refraction, and reflection fromwater bodies and terrestrial objects such as mountains and buildings.Positioning and navigation can be degraded in urban environments bymultipath, and the error can increase considerably if not properlycompensated. In situations where the line-of-sight (LOS) is obscured bysurrounding buildings, the receiver may still be able to navigate byusing the non-line-of-sight (NLOS) signal, which originates from singleor multiple reflections/diffractions of the GNSS signal. The use of 3Dmodels has been one of the preferred solutions to recreate the multipathenvironment as seen by a GNSS device. This solution brings thecapability to generate a multipath signature that is representative ofthe position of the antenna in a specific time and space. For the given2D point 365, a +/−3-meter error in the map data yields an error of 7.85minutes in the determination of a GPS satellite being line-of-sight ornot line of sight. Over a forecast period of 180 minutes this wouldprovide a confidence interval of 96%.

Next, examples of two modes of analyzing satellite visibility (a 1-passexample, and a 2-pass example) are disclosed.

1-Pass/2-Pass Analysis of Visibility

The following two examples of satellite visibility analysis use twoassumptions for simplicity. The first assumption is that the satellitehas a speed of 3.9 kilometers per second and a nominal sidereal time of11 hours 58 minutes and 2 seconds (in other words, 43,082 seconds). Inpractice, other satellite speeds may be used, not just these for GPS.The second assumption is that there are pi radians (180 degrees) wherethe satellite is not obstructed by the Earth itself. In practice, thenumber and angle of rays sampled are based on factors such as curvatureof the Earth, terrain features, and satellite elevation/azimuth.

Both examples rely on ray tracing or casting over a Digital SurfaceModel to determine whether or not the ray is obstructed.

FIG. 4B illustrates an example 1-pass analysis for finding satellitevisibility at a Valid Observable Point (VOP). Diagram 400 b includesorbital segment 418, VOP 435, rays 430 b-1, 430 b-74, 430 b-75, 430b-21,541, and obstructions 440 a, b. Diagram 400 b provides a quickoverview of the 1-pass process to aid understanding of the 2-passprocess.

Orbital segment 418 is the part of an orbit of a GNSS satellite (notshown here) that is guaranteed not to intersect the Earth based oncurvature of the Earth, elevation/azimuth. Thus orbital segment 418represents satellite positions that should be analyzed for potentialvisibility from VOP 435.

VOP 435 designates a location for which satellite visibility withrespect to orbital segment 418 is being determined.

Rays 430 b-1, 430 b-74, 430 b-75, 430 b-21,541 designate several of therays corresponding to each position of the satellite that is analyzedfor visibility. The number of rays is determined by the number ofsamples being taken along orbital segment 418. In this example, theanalysis is made for the change in satellite position every second, andthus a ray for every second (21,541 in total) is required. Thatcorresponds to 0.000135 radians (or 0.0077 degrees) per second.

Obstructions 440 a, b are visually depicted as buildings. In practice,obstructions 440 a, b are not limited to buildings, but may be any formof static or dynamic artificial obstruction (e.g. walls, trucks) ornatural obstruction (e.g. distant mountains, nearby sheer cliffs,vegetation).

Each ray is analyzed to see if the ray would reach the satellite thusestablishing Line of Sight (LOS), or whether the ray would be obstructedby obstructions 440 a, b and thus has no Line of Sight (NLOS). Thestatus of each ray is stored.

A characteristic of the 1-pass analysis is that the analysis providesfine detail of obstructions. On the other hand, some of that analysis isredundant. For example, rays 430-1 through 430-74 are obstructed, andmost obstructions would not permit visibility from the ground up to430-74. By contrast, ray 430-75 has LOS because the ray extends toorbital segment 418 without intersecting obstruction 440 a, and thatlasts through ray 430 b-21541.

Thus, it may be more efficient to first sample orbital segment 418 usingcoarser angular intervals between rays. After finding the rays thattransition between LOS and NLOS at a coarse resolution, performing apass of analysis only around a coarse ray close to the point oftransition using finer angular resolution.

The next two figures describe an example of a 2-pass analysis. The firstpass, using coarse angles between rays, is described in more detail inthe next figure.

FIG. 4C illustrates the 1st pass of an example 2-pass analysis forfinding satellite visibility at a Valid Observable Point (VOP). Diagram400 c includes orbital segment 418, VOP 435, coarse rays 430 c-3, 430c-4, 430 c-1046, 430 c-1047, and obstructions 440 a, b.

The description of orbital segment 418, VOP 435, and obstructions 440 a,b are similar to those of FIG. 4B, above, and the reader is referred tothose sections for more detail.

Coarse rays, such as 430 c-3, 430 c-4, and 430 c-1046, 430 c-1047 arealso traced or cast, but at much larger angle intervals. The analysisdefines coarse ray angle interval such that the coarse rays can detect a3 m building (slightly less than 1 story, which is approximately 3.3 m)that is 1 km away, as illustrated by obstruction 440 b. With the initialassumptions provided for these examples, the analysis requires 1048 rayswith an approximately 0.003 radians (0.1719 degrees) change.

The first pass performs a sweep that is similar to the 1-pass analysis,but using the coarse ray angular intervals between each of 1048 coarserays. The samples taken by the coarse ray sweep are analyzed to findpairs of successive coarse rays where the pairs transition from LOS toNLOS, or vice versa. Here in FIG. 4C, the status transitions between thepairs of coarse rays <430 c-3, 430 c-4> and <430 c-1046, 430 c-1047>.Where a pair of successive coarse rays transition, the pair is noted forfurther analysis in the next pass.

Details of the second pass, with finer angles between rays, are shown inthe next figure.

FIG. 4D illustrates the 2nd pass of an example 2-pass analysis forfinding satellite visibility at a Valid Observable Point (VOP).

Diagram 400 d includes orbital segment 418, VOP 435, fine rays 430 d-74,430 d-75, 430 d-21499 and 430 d-21500, and obstructions 440 a, b.

Again, the descriptions of orbital segment 418, VOP 435, andobstructions 440 a, b are the same as described in FIG. 4B, and thereader is referred to that section.

Fine rays, such as 430 d-74, 430 d-75, 430 d-21499, and 430 d-21500, aretraced or cast during the second pass such that the fine rayscorresponding to one of the pairs of successive coarse rays noted duringthe first pass.

For each pair of coarse rays that were noted to transition status, oneof the coarse rays (here, the NLOS ray), is designated. The fine raysare cast or traced at angles relative to the designated coarse ray. Fineray angle intervals are set to provide the same accuracy as in the1-pass example. For ease of comparison between the 1-pass example andthe 2-pass example, the fine ray angular interval is the same rayangular interval as the 1-pass example: 0.000134 radians (0.0077degrees, or 1 second of movement). Other implementations that follow thegiven example may set other fine ray angular intervals. In general, thefine rays angle interval is less than the coarse ray angle interval.

Briefly referring back to FIG. 4C, since <430 c-3, 430 c-4> are adesignated pair of successive coarse rays, fine rays are cast betweencoarse rays 430 c-3 and 430 c-4. Similarly, fine rays are cast between430 c-1039 and 430 c-1040. Returning now to FIG. 4D, fine rays, such as430 d-74 and 430 d-75, are cast between coarse rays 430 c-3 and 430 c-4.Likewise, fine rays such as 430 d-21499 and 430 d-21500 are cast betweencoarse rays 430 c-1046 and 430 d-1047.

Transitions of status of fine rays in 2nd pass are used to determine thedimensions of obstructions 440 a, b. Here, for the 3 m obstruction, fineray 430 c-21499 has visibility and fine ray 430 c-21500 is occluded. The3 m height of obstruction 440 b can be approximated from thatinformation. The forecast system saves an indication of the time inwhich visibility transitions between LOS and NLOS.

In some implementations, fine rays are incrementally cast or tracedrelative the NLOS coarse ray of the designated pair of successive coarserays (in other implementations, relative to the LOS coarse ray) oneither side of the coarse ray until a stopping condition occurs. Onepotential stopping condition is that the corresponding successive coarseray (or the endpoint of orbital segment) is met or exceeded. Anotherpotential stopping condition for casting or tracing may be that a statustransition is detected at a fine ray.

The efficiency advantage of the 2-pass approach over the 1-pass shouldbe apparent. The number of coarse rays used in 2-pass analysis to samplethe satellite orbit can be a magnitude smaller than the number used inthe 1-pass. From the coarse analysis, the last “hit” will then beprocessed further to fill in the gaps and find the exact second thesatellite transitioned between LOS and NLOS. This permits fewer overallrays to be cast while maintaining a high level of accuracy.

Using the example, the coarse rays are spaced every 0.003 radians andthe second pass will process a fine ray for every 0.0077 degrees (everysecond). In the described implementation, fine rays are cast or tracedon either side of the coarse-ray hit, so, up to an additional 22 finerays on either side (44 additional fine rays in total) of the coarse rayfor each detected object. The sum of fine rays and coarse rays that arecast or traced in both the 1^(st) and 2^(nd) pass of the 2-pass approachis still far less than the brute force scan of 21,541 rays in the 1-passapproach.

Although the example of the 2-pass analysis discusses coarse ray angularintervals with a key performance indicator (KPI) of detecting 3 meterhigh features 1 km away, in some implementations, other KPIs may be usedto a set coarse ray angular interval where necessary or more suitablethan the given 3 m/1 km KPI.

Besides casting or tracing of fine rays at successive angular intervals,another approach may be divide and conquer (in some implementations withrays at the determined angular intervals, but in others, may be atvariable angles). Where parallel processing of rays is possible, finerays may be cast or traced at respective angles in a batches of one ormore rays.

FIG. 5 shows message flow for session authentication between theuser/customer 348, content delivery network (CDN) 369 and GNSS Forecastengine service (FES) 348 that provides forecasts to CDN 369. Devices usean auth API key for authentication and a set of API methods (device API)to download GNSS Forecast data for a particular space and time. DeviceAPI is utilized to process the request, selecting a set of cuboids fromSpatial DB and providing links where these cuboids could be downloadedfrom CDN 369. Front end 512 delivers the user experience UX. Userhandling 514 handles requests for authorization for users. Once asession is open between customer 348 and CDN 369, DB and file cache 516responds to API requests.

GNSS Forecast CDN 369 processes uploaded data in a queue in GNSSForecast data processor, updates the metadata spatial database andreplicates GNSS Forecast data based on cuboid position to appropriateCDN origin. After this occurs, the forecast data is available to devicesfor download. If a certain cuboid will be required, this will bedelivered through the CDN Edge and cached there for later re-use.

Improving Receiver Performance

FIG. 6A illustrates the block diagram for a receiver, showing the flowof data from Forecast Assured Navigation (FAN) to elements in the GNSSreceiver. A typical GNSS receiver consists of a number of hardware andsoftware components involved in reception, demodulation and decoding ofsatellite signals; and then the use of these to generate a positioningsolution. Position, velocity and time, and additional items may beproduced. Upon receipt of a GNSS Forecast, GNSS receiver searches forthe IDs of visible satellite, acquires code delay and Doppler estimates,with rough alignment of code and carried, refines code and carrieralignment and completes pseudorange and data demodulation measurements.Typical GNSS receiver components for the disclosed technology includeantenna 622, GNSS RF 632 and GNSS hardware 633 for receiving, acquiringand measuring GNSS signals. The direction of this is typically undercontrol of the Measurement Engine (ME) 634 software. Positioning Engine(PE) 636 uses the data produced from ME 634 to compute the position ofthe device. Fused Positioning Engine (FPE) 638 may also use themeasurement data from the ME 634, with non-GNSS position data 628 fromother positioning services, to compute the position of the device, alsoreferred to as a positioning solution. Antenna 622 captures GNSS signalsand feeds them to GNSS RF 632, for use by GNSS hardware 633 for findingsatellite signals and determining the satellite name and decoding theposition of the satellite.

FAN service 672 passes the forecast from the FAN service via thecommunication layer 676, through the interface layer 656, directly topositioning engine (PE) 636 with RTK and fused positioning engine (FPE)638 with RTK, in one implementation. The forecast includes the positionsand augmentation services, enabling the determination of a preciseposition fix. The flow of the disclosed forecast data can be implementeddifferently way in a different embodiment. RTK systems use aground-based reference station at a fixed, known location to process andtransmit the error-corrected signal to a receiver in a moving vehicle.It is generally accurate to the centimeter level, and works in realtime, but each reference station only has a range of 10-20 km.

In some cases, the disclosed GNSS Forecasts can be applied to improveGNSS positioning engine (PE) 636 performance. The PE 636 is the functionthat computes the position of the device. It receives GNSS signalmeasurements and satellite orbit data and may also receive otherinformation, including the data generated by the prediction methodsdescribed in this document, and combines these to determine position.This procedure includes use of data and quality information provided bya Measurement Engine (ME) 634, as well as internally generated qualityinformation, to determine how best to use and combine the availableinformation, including satellite measurements. A GNSS PE 636 or receivercan use a GNSS Forecast to improve its performance, including integrityand reliability of the position solution, time to fix, positioningprecision and also accuracy.

GNSS positioning engines and receivers are regularly determining whatsatellites to track and use as a part of their position and timecalculations. With predicted information of the localized environmentalimpacts from buildings, vegetation, terrain and other sources ofobstructions and multipath, the GNSS receiver can know in advance whatGNSS satellites are better to use and hence improve the integrity andreliability of the position solution calculations. The GNSS Forecastdata provides information on which satellites are visible—line of sight(LOS)—and which are not. Line of sight visibility is a good indicator ofmeasurements that are likely to be less affected by signal multipath, orother errors and hence can be used in the PE 636 or FPE 638 with agreater weight than other satellites which are not line of sight.

For example, the GNSS PE 636 or FPE 638 may use the GNSS Forecast dataas described next. The GNSS Forecast data is provided to the GNSSreceiver PE 636 or FPE 638. Note that the FPE may be a differentsoftware entity outside the GNSS receiver, in which case the GNSSForecast data may be provided to either, or both of the PE 636 and/orFPE 638. The GNSS Forecast data contains information on which satellitesare line of sight and which are not line of sight, for a specifiedposition and a specified time. At this point, the PE 636 and/or FPE 638may determine to use this information to increase the confidence withwhich it uses the line of sight data, by comparison with the non-line ofsight (NLOS) data. A typical method for this would be to adjust therelative weighting on the LOS versus NLOS measurements. This may be doneby adjusting the individual satellite's estimated—a priori—standarddeviation, or variance to reflect the difference between LOS and NLOSmeasurements. One method for this would be to increase (make larger) thestandard deviation, or variance, of the NLOS measurements, and leave thestandard deviation, or variance, of the LOS measurements at the valuewhich had previously been determined by the GNSS PE 636 and/or FPE 638.It is also possible to perform the reverse—i.e. to decrease, or reducethe standard deviation, or variance, of the LOS measurements, but thishas a potential to result in a position which may become heavily biasedto the LOS measurements, which is not desirable.

The GNSS Forecast data may be provided for all satellites, based uponthe environment in which the GNSS receiver is operating; or asinformation on individual satellite signals; or both. The use of GNSSForecast will enable the receiver to apply “environmental multipliers”to measurement variances with increased confidence based upon theenvironment in which the GNSS receiver is operating; or as informationon individual satellite signals; or both and provide a higher level ofintegrity in this aspect of the positioning process.

For example, the GNSS PE 636 and/or FPE 638 may use the GNSS Forecastdata to adjust the standard deviation, or variance values of eachsatellite that is being measured, as described above. It may further useinformation in the GNSS Forecast for providing information on the localsignal environment, or use the data provided to determine an estimate ofthe local signal environment, and apply a common effect to the standarddeviation, or variance of all satellites. For example, the GNSS receivermay be operating in a dense urban environment, where the typicalexpectation is for worse GNSS signal reception, in which case the PE 636or FPE 638 may apply a multiplier to all of the a priori standarddeviations, or variances, to increase them all (make larger) by a factorwhich would reflect the worse signal environment, using the GNSSForecast data to determine this. In a similar fashion, if the signalenvironment had been supplied as Rural, then it would be possible to usethe multiplier to reduce (make smaller) the standard deviation, orvariance values.

The GNSS Forecast will help the GNSS positioning engine 636 to improveits signal quality monitoring algorithms by providing information onwhich measurements are contaminated and how badly contaminated eachmeasurement is. As described above, the environmental multiplier may beapplied uniformly as a value to increase, or decrease the standarddeviation, or variance on all satellites. The GNSS Forecast data isprovided on a per satellite basis. It is therefore also possible toapply a multiplier on a per satellite basis, dependent upon dataprovided about this satellite signal at a specific location and aspecified time. The multiplier may be a uniform one related to the setof LOS, versus the set of NLOS satellite observations, or it may differon a per satellite basis, depending upon the level of signalcontamination that is predicted for each satellite by the GNSS Forecast.

This information can then be used by the PE 636, for example to choosethe most appropriate positioning algorithm to use; to de-weight orexclude measurements from the computation of the position solution tominimize the impact of multipath-induced errors in the positionsolution; and to improve the recovery from lead and lag events normallyseen during dense urban driving. As described above, this can beperformed by using the GNSS Forecast data to adjust the a priori signalstandard deviation, or variance values. This adjustment to the relativeweighting will then alter the position determination calculation, whichcan then improve the precision and accuracy of the resulting position,and hence improve performance in GNSS signal environments. The use ofGNSS Forecast can also be used to improve the position solutionintegrity and reliability, by providing externally generated data tosupport decisions and calculations made by the GNSS receiver's PE 636.

In another case, the disclosed GNSS Forecasts can be applied to improveGNSS Measurement Engine (ME) 634 performance. A ME 634 is the functionthat interfaces with the GNSS receiver hardware 633 and performs anumber of functions including: the search for, and acquisition of,satellite signals; the decoding of data transmitted by the satellites;tracking of multiple GNSS satellites and the generation of measurementsfrom this data, including pseudorange (the estimated distance from thereceiver to the satellite), Doppler frequency and carrier phasemeasurements and associated quality information such as signal level, orcontinuity of signal tracking. These data are supplied to the PE 636and/or FPE 638, which use them in the position calculation. A GNSS ME634 or receiver can use a GNSS Forecast to improve the performance(integrity and reliability of the position solution time to fix,positioning accuracy, etc.) as described above. GNSS ME 634 in a GNSSreceiver is regularly determining what satellites to acquire, track anduse as a part of their position and time calculations. With predictedimpacts of the localized environmental (from buildings, vegetation,terrain and other sources of obstructions and multipath) the GNSSreceiver's ME 634 will know in advance what GNSS satellites to use, howthe satellite visibility will change depending upon the receiver'slocation and hence how to adjust their signal acquisition and trackingalgorithms to make use of this information. Details of how this may bedone are described next.

The GNSS Forecast data would help the GNSS ME 634 to improve its searchstrategy algorithm, its channel allocation and management algorithms toquickly acquire signals as they become visible or dismiss signals oncethey become obscured. The GNSS Forecast data provides information oneach satellite's LOS, or NLOS status for a specific location and aspecific time. This data may also be provided for another locationand/or time, as requested by the GNSS receiver. Hence the GNSS ME can beprovided with the GNSS Forecast information that will allow it to knowwhere and when an individual satellite will be LOS, or NLOS. Thisinformation allows the ME 634 to do the following. (1) Reduce the timeand frequency uncertainty on the LOS signals—or conversely increase thetime and frequency uncertainty on the NLOS signals. This will allow theME 634 to direct the GNSS receiver hardware on which satellites areavailable, and when, and the amount of resource to expend in acquiringthem. (2) Adjust the amount of signal integration that is needed for anindividual satellite—for example if the satellite is confirmed as NLOS,then it is likely that this is a lower signal strength and hence thesignal integration time may be increased. (3) Note that, as the GNSSreceiver PE 636 and/or FPE 638 may also know the route that the GNSSreceiver is travelling, they can use the GNSS Forecast data to know inadvance how to adjust the signal integration time to maintain signalreception—if the satellite signal transitions from LOS to NLOS, thesignal integration time can be increased, whilst if the satellite signaltransitions from NLOS to LOS, then the signal integration time can bereduced. This may also be required to avoid the potential for the signalto saturate, depending upon the magnitude of the change in signal level.This would improve the accuracy of the position solution especially indense urban environments. The GNSS Forecast data would also be used toimprove the robustness of the multipath indication algorithm byproviding information on (a) which measurements are contaminated, and(b) how contaminated each measurement is. This can also be used toimprove the position solution integrity and reliability, by providingexternally generated data to support decisions and calculations made bythe GNSS receiver's ME 634. The use of this data is described above.

In yet another case, the disclosed GNSS forecasts can be applied toimprove GNSS Real-Time Kinematic (RTK) performance. RTK systems use aground-based reference station at a fixed, known location to process andtransmit the error-corrected signal to a receiver in a moving vehicle.The provision of GNSS forecast data can be used to improve theefficiency and reliability of ambiguity resolution, which is required togenerate the highest precision GNSS carrier phase measurement-basedpositioning solution. With predicted impacts of the localizedenvironmental (from buildings, vegetation, terrain, and other sources ofobstructions and multipath) the RTK solution can know in advance whatGNSS satellites to use and how the satellite visibility will changedepending upon the receiver's location. This information can be used toaid in (1) detection of cycle slips, and (2) determine which satellitemeasurements are the most reliable, both for position calculation andalso in the ambiguity resolution process, for example which should beused as primary satellites in an ambiguity resolution process. This canbe used to improve the time to fix, accuracy, stability and integrity ofthe RTK positioning solution. For example, satellites which are forecastto be line of sight would be used in the first attempts to resolveinteger ambiguities in an RTK solution—or to reduce the integer domainambiguity search space, through application of techniques detailedabove, related to the uncertainty of the observations, but now appliedto the integer ambiguity, or phase measurement domain.

The disclosed GNSS Forecasts are also usable in a lab testingenvironment, also using the methods described above.

FIG. 6B illustrates an example result of utilizing the disclosed GNSSForecast with RTK to improve performance. The improved route is shownusing a dotted line 662 as a more reliable path compared to the routeshown using the solid lines 664.

A similar approach to that described above for PE position calculation,or RTK ambiguity resolution may also be applied to aid in Precise PointPositioning (PPP) time to converge the position solution.

Precise Time Synchronization of Distance-Separated Locations

The disclosed technology for GNSS Forecasts can improve timesynchronization of distance-separated locations. One use of GNSS is toprovide timing and synchronization to systems such as telecommunicationsnetworks. Unlike vehicles and consumer devices, these systems aretypically at a fixed location and do not move. This makes it morestraightforward for these systems to know where GNSS satellites are andthe typical behavior, since the environment is mostly unchanging/static.However, when deploying a new system, a GNSS Forecast can aid indetermining the best antenna placement. For existing installations, aGNSS Forecast can provide updates to changes in the environment like newbuildings, signs, and other obstructions, which can be orders ofmagnitude larger, in terms of a comparison of the magnitude of theimpact of these errors. It can also predict where multipath, and aresulting timing delay. may be present if the calculated position of thestatic object shifts unexpectedly due to a lack of sufficientline-of-sight satellites during a certain time of day or time periodwithin a day.

The disclosed Forecast Assured Navigation (FAN) technology includes themethodology of testing multiple receivers and antennas as a function oflocation, altitude, and flight envelope. While testing of GNSSreceivers/antennas in a simulated or lab environment has advancedgreatly, when a component is being used in a life critical environment,such as flights carrying passengers or flights over people, it isadvantageous to test the actual performance in a real-world scenario. Areceiver that is being used on an Unmanned Aircraft (UA) faces uniquechallenges in that it is subject to altitude changes as well asacceleration, deceleration, velocity changes, turns, climbs, descents,rotations and vibration. The disclosed methodology allows a receiverunder test to be evaluated under these real-world conditions. In oneimplementation, the disclosed technology includes the methodology ofcarrying multiple receivers on a purpose-built drone and being able tocompare their performance to ground truth. This enables one to assurethat the receiver/antenna will perform as expected when relied on forposition, navigation and timing.

In yet another use case, the disclosed technology for GNSS Forecasts canbe applied to GNSS receiver development. GNSS receiver developers canuse GNSS Forecast to find locations that are challenging to GNSSoperation. GNSS receiver developers could then test their receiver basedon the real world or synthetic 3D environment of these locations. Theuse of the GNSS forecast data detailed above can be used to improve theGNSS performance by enabling adjustments to be made to the GNSS receiverME 634, PE 636 and FPE 638 algorithms, for example the changes to therelative weighting, based upon changes to the individual satellites'standard deviation, or variance values.

Improving Sensor Function & Cellular Station Location and AntennaPlacement

In yet another case, GNSS Forecasts can be applied to improve sensorfunction. In Automotive applications, the GNSS sensor is often assignedthe lowest priority within the positioning engine and primarily used tosanity-check the solution for absolute position. This is true for bothlane level positioning applications and navigation applications. Due tothe effects of multipath acting on an unknown number of the GNSS signalsreceived, its position accuracy and integrity can be negativelyaffected. GNSS Forecasts can aid the GNSS engine in predicting whichGNSS signals are line of sight and which are affected by multipath,therefore allowing to receiver to lessen its error bounds and increaseits integrity. As a result, the positioning engine (PE) 638 can knowwhen it can assign GNSS a higher priority to improve overall absolutepositioning accuracy.

Another use case example includes applying the disclosed GNSS Forecasttechnology to determine cellular base station locations andconfigurations. One of the most critical parts of wireless communicationnetworks is the time and synchronization of the network and devices toensure proper transmission and handoff between base stations. One of themost common methods of achieving synchronization and timing is the useof GPS/GNSS at the cellular base station. Hence choosing the bestlocation for a cellular base station and configuration includes ensuringgood GPS/GNSS coverage and antenna placement. The disclosed GNSSForecast can be used to help choose the cellular base station locationand antenna placement.

Jamming or Spoofing of GNSS Signals

Disclosed GNSS Forecasts can be applied to identify spoofing attempts,to determine jamming or spoofing of GNSS signals, to improve GNSSintegrity and to improve anti spoofing. A comparison of measurementsagainst predicted signal availability, often for multiple satellites andconstellations, can improve interference and spoofing detection andmitigation. An obscured satellite, even if received via multiple ordermultipath, will typically have a lower signal strength. A satellitesignal received at normal or just below expected strength, when thesatellite is known to be obscured, is an indicator of spoofing. Theassumption is that the spoof checking is an integral real-time constantcheck, instead of a one-off activity. Note, that if the receiver isalready in a spoofed position, and the spoofer happens to also use thedisclosed Forecast Assured Navigation service or has a similar mechanismto provide only line of sight satellite signals, this can aid thespoofer. Starting from a known trusted (correct) location, the receiverconstantly corrects its navigation position using additional aids, andenhanced with the disclosed GNSS Forecast, to assure that spoofing isnot happening.

In one implementation, a disclosed method of determining jamming orspoofing of GNSS signals includes receiving a GNSS Forecast and coveringa time and location of present operation of a vehicle, including areliability measure for respective GNSS signals received from aplurality of satellites at the location. The disclosed method alsoincludes comparing received GNSS signals received from the plurality ofsatellites to the GNSS Forecast, and rejecting outliers detected by thecomparing and rejecting the GNSS signals from the satellites as jammedor spoofed. In one example, a received signal with a strength levelsignificantly higher than expected may indicate that the signal is froma satellite source that is likely to be a spoofing source. Additionally,a signal may be rejected because the satellite is known to be obscuredfrom the GNSS receiver and therefore must be a false signal.

In another implementation, a disclosed enhancement includes determiningthe expected vs measured Signal to Noise Ratio (SNR) or Received SignalStrength (RSS). Where SNR is weaker than predicted, the receiver may bejammed. Where the SNR is stronger than predicted, the receiver may bereceiving spoofed signals.

The SNR/RSS for a GNSS satellite may be predicted, in part, based onelevation angle. Examples of prediction is shown in FIG. 6C.

FIG. 6C illustrates SNR of GPS signals at corresponding elevations on anopen field, using real world data.

As shown in the figure, a satellite that is close to directly above areceiver will tend to provide a tighter and higher range of SNRs thanone that is at a lower angle.

Other factors that play a role, that are not directly labeled in FIG. 6Cinclude antenna quality and obscuration.

An expected SNR may also vary based on the quality of antenna. Forexample, an expected SNR for a theoretical signal of 130 dBm may be42-44 dB. For a high end receiver with a good antenna, this can rise to50 dB. Most handheld phones do not have a very high end receiver withgood antenna. An expected SNR for the handheld device might peak at 35or 40 dB.

An expected SNR/RSS of GNSS satellites may also vary based on whether avisible GNSS satellite is has line-of-sight (LOS) or non-line-of-sight(NLOS) visibility. With other factors being equal, a GNSS satellite withLOS is expected to have a better signal than a GNSS satellite that isobscured and thus has NLOS. The LOS satellite will have a higherSNR/RSS.

A device attached to the receiver will receive signals from varioussatellites. The device will also receive, the elevation/azimuth of thesatellites and obscuration, as part of GNSS forecast data. Based on theLOS/NLOS status of the satellite, the device can predict an expectedSNR/RSS.

The predicted SNR/RSS of each satellite is then compared with theacquired SNR/RSS to detect if spoofing or jamming has occurred. Forsatellites with a predicted SNR/RSS that is high relative to acquiredSNR/RSS, those satellites may be jammed. Where acquired SNR/RSS ishigher than predicted SNR/RSS, then the satellites may be spoofed. Notethe above description of a comparison encompasses does not constrain thecomparison action to an analysis of a single satellite. While thecomparison could be for a single satellite, but could also encompass asequential comparison of single satellites, or a comparison of a batchof satellites, or the aggregate characteristics of a group ofsatellites. The comparison action may occur with some or all of thesatellites visible from a point.

Although the actions of receiving a forecast, calculating a predictedSNR, comparing the predicted SNR to an acquired SNR are described aboveas sequential steps, those steps may occur at multiple points over time.The detection may also be based in part on analysis of the earlierresults of the comparing action and the later results of the comparingaction.

When detection of spoofing or jamming has occurred, attached devices mayreact in one or more manners. Devices may rely on positioning systemsother than GNSS-based systems. Devices may also report the indication ofspoofing or jamming for analysis and potential reporting to otherdevices with regards to the potential attack.

Thus, if a receiver, prepared with GNSS forecast data, knows that itshould only receive a SNR of 20 dB at most based on the handheld phonelocation and the satellite location, but the measured SNR is closer to45 dB, then it is likely that the signal is spoofed. Likewise, if theexpected SNR is 20 dB but the detected SNR is closer to 10 dB, then itis likely that the signal is jammed.

The values of different SNR used in the examples may differ in differentcircumstances or for different implementations.

Far Away Features with Significant Obstruction

FIG. 6D illustrates a scenario in which a large obstruction is distantfrom nearby grid of points and is still accounted for by the forecastsystem.

Map 600 includes the elements of satellite 602, skyscraper 612, vehicle632, background 623, and foreground 633.

In this scenario, a signal from GNSS satellite 602 is occluded byskyscraper 612 from having line of sight (LOS) with a receiver invehicle 632. Skyscraper 612 is 500 meters (m) distant from vehicle 632.That distance is beyond the normal range of adjacent areas provided tothe onboard receiver of vehicle 632, and so would not be included in aGNSS forecast that focuses on only areas in foreground 633. Ifskyscraper 612 is not accounted for as an obstruction, then GNSSforecast data may erroneously indicate or state that satellite 602 hasline-of-sight (LOS), when in practice, satellite 602 has noline-of-sight (NLOS).

To account for monumental obstructions that are distant to vehicle 632,a forecast system will provide data about obstructions from distantbackground locations such as background 623. Unlike obstructions from agrid of points in foreground 633, it is often sufficient to providecoarse resolutions. For example, a 50 m difference in a mountain that is5 kilometers (km) away is unlikely to significantly alter the results ofwhether a satellite has LOS or NLOS visibility. Similarly, a 10 mdifference in skyscraper 612 that is 500 m away from vehicle 632 is alsounlikely to significantly alter the results of whether a satellite hasLOS or NLOS visibility. This presents an opportunity to reduce the sizeof forecast data being transmitted, and the amount of forecast data tobe processed at the receiver size, while still increasing accuracy byaccounting for distant obstructions.

Obscuration can be correctly modeled for large objects in distantbackground 623 to vehicle 632 by using a coarser resolution than fineresolutions used to model objects in close foreground 633. Here, while a3D model for foreground 633 will continue to use a fine resolution (suchas 5 cm to 1 m cuboids in the volume under consideration, but foregroundcuboid resolutions may range from 2 cm to 5 m without departing from thespirit of the disclosure), 3D models for background 623 may instead berepresented by cuboids with a coarser resolution of 25 m-30 m cuboids(while in some implementations, a broader range of 10 m through 50 mcuboids in the volume under consideration may be used). In someimplementations, different ranges resolutions for foreground 633 andbackground 623 may be used.

Once the 3D models from foreground 633 and background 623, a correctvisibility of satellite 602 from the grid of points is determined andreturned responsive to a corresponding query. A correct finding of NLOSvisibility will be returned rather than the incorrect LOS visibility.

Although the above uses skyscraper 632 as the monumental object, inpractice, other kinds of very large objects may also be accounted for.Other manmade monumental objects such as sports stadiums oramphitheaters, natural terrain features such as hills, mountains, andeven vegetation (such as giant sequoia and redwood trees) are examplesof objects that may obscure GNSS signals from a far distance, and can beaccounted for by the technique described above.

Although the distance between foreground 633 and background 623 isprovided in the example as 500 m, the distance between foreground andbackground may be different. In some implementations, the distance mayfall in a range between 400 m and 5 km.

Autonomous Ground-Based Vehicles

The disclosed technology for Forecast Assured Navigation (FAN) generateGNSS Forecasts for navigation. GNSS Forecasts provide a predicted GNSSavailability and performance for an area and time, which is usable inadvance or in real-time for any vehicle or system using GNSS. As set up,the positioning requirement for the drive can be selected. Before aplanned journey and/or in real time, a vehicle sends its location andintended routing request to the cloud service. The service provides aGNSS heat map, also referred to as a Point Cloud, which is then used todetermine the quality of GNSS coverage along the route and nearby areas.For example, a vehicle may need a specific level of GNSS performance tohave safe automated lane control. The vehicle may use a GNSS Forecast todetermine if the vehicle will have adequate GNSS signals on the roadahead. If so, the vehicle will be able to continue operation and lanelevel control. Alternatively, if GNSS is forecasted to not be adequate,the vehicle may change its automation mode, rely on other sensors,return control to the driver or trigger return of control to the human.

Level 2 to Level 5 Autonomous Vehicle Operation requires knowledge ofthe vehicle's Precise Position. In Lane Level Accurate Positioning, theaccuracy requirement is commonly defined as 0.3 Meters HorizontalPosition and having 99.99% certainty in that Position. It is MissionCritical that the vehicle's Positioning Engine retains that level ofaccuracy throughout its journey. In one example, if a DOP is 2.0 isrequired for lane level accuracy of 0.5 m, FAN can predict where andwhen the required DOP will be available to safely utilize the desiredautonomous driving (AD) features.

In one example use case, an autonomous ground-based vehicle is travelingand wants to know the expected GNSS signals on its route of travel. Thevehicle connects to a cloud service to request the GNSS signalpredictions along its route. The cloud service processes the requestfrom its distributed architecture to get the appropriate forecastinformation for the vehicle and transmits it to the vehicle. The cloudarchitecture interfaces with a GNSS Forecast engine service that gatherssatellite data, environmental data, and maps to process with variousalgorithms to provide a prediction and then publishes the prediction ona cloud architecture.

The disclosed GNSS Forecast can also be used to improve fleetmanagement. Fleet management commonly uses GNSS as the primary sensorfor location tracking. In an urban environment, real-time metrics, andguidance such as estimated arrival time and taking the mostfuel-efficient route are adversely affected if the vehicle is placed inthe incorrect lane or side of the intersection. GNSS Forecasts can allowthe tracking algorithm to prioritize high confidence GNSS locations andreject low confidence results to improve overall vehicle trackingaccuracy, and can also use this information to alert the driver todouble check their guidance and follow local road signs if a lowconfidence area is ahead. Similarly, asset tracking for shippingcontainers, construction equipment, rail cars, and other expensiveassets that are large enough to justify GNSS tracking rather than justRFID. GPS is used to track assets in transit. Reliability of the signalsthrough use of the disclosed GNSS Forecasts can enable theidentification of areas of concern, due to poor GPS performance, such asknowing where cargo containers need to be placed in a cargo port.

Path Planning

The disclosed technology for GNSS Forecasts can be applied to pathplanning for ground-based vehicles, in another use case. Autonomous andautomated vehicles use a variety of sensors to determine the relativeposition of the vehicle to its surroundings, sometimes calledlocalization. GNSS is typically the primary method of determiningabsolute position and time with respect to the surface of the earth, orgeodesic reference, and time (Universal Coordinated Time (UTC)). Thereliance of the vehicle on GNSS makes it very important or critical tothe operation of the vehicle. GNSS Receiver performance can be enhancedusing RTK (RTK, PPP, & RTK+PPP), WAAS, SBAS, GBAS, and RAIM technologiesand services. While these techniques improve the performance of GNSS,none of them provide geospatial awareness information about thelocalized environmental impacts on the GNSS signals from buildings,vegetation, terrain, and other sources of obstructions and multipath.

Using a GNSS Forecast, a vehicle can plan its path/route to ensure thatit has the best GNSS signals available and know where and when itsperformance will be impacted. The system can make trade-offs betweenGNSS performance and other sensors and system that can augment or bridgegaps in GNSS coverage. The system can make trade-offs between drivingmodes; for example, the system can use a GNSS Forecast to determine apath that has the highest utilization of autonomous or automated vehicleoperation versus fastest route, shortest route, or economic route. Thisis not limited to on-road vehicles like cars and trucks. This can beused in mining to determine where best to operate machinery based onGNSS Forecast and the time of day. In one example, mining on one part ofthe mine where signals are good at 9 am versus another part of the mineat 10 pm. The GNSS Forecast can also be used to determine the impact ofthe change in the environment due to blasting/mining.

GNSS environments for any vehicle at ground level or low altitudes arehighly dynamic. Traversing complex GNSS environments only minutes aheador behind the planned time of travel can have a major impact on the GNSSperformance. A vehicle traversing through an intersection in a denseurban environment ten minutes earlier can result in ‘okay’ or safe GNSSsignals rather than poor signals.

Aviation Use Cases

In one aviation use case, a drone flying needs to know the expected GNSSsignals on its route of travel, or area of operation, for safenavigation. This is especially important for Beyond Line of Sight of thepilot (BLOS), including package delivery or inspection. In order todetermine if safe navigation using GNSS is possible, a prediction of thesignal strength can be used to determine the best flight path (includingaltitudes), take-off and landing areas, areas to hover or loiter,risk-ratio of the flight, if areas of poor GNSS can be traversed usingmitigation techniques and for how long. In some cases a drone may usecertified avionics, like those certified using Federal AviationAdministration (FAA) Technical Standard Order (TSO)-C199 for TrafficAwareness Beacon System (TABS). The certification requires the system toachieve specific levels of positioning performance, like sectionA1.2.6.3 requires 30 meters horizontal position error with a HDOP of 2.5or less. The disclosed GNSS Forecast solution can be used to determineif the flight path or area in which a drone is going to be flown in thefuture will have the required DOP, resulting position accuracy, andcompliance in operation. Similar positioning requirements exist withinFAA remote identification rules.

In order to access a GNSS prediction or forecast, the drone, flightplanning software, ground control software, Unmanned Service Supplier(USS), or Unmanned Traffic Management (UTM) system connects to a cloudservice to request the GNSS signal predictions along the intended route,area or operation, or an area where a route needs to be determined. Thecloud service processes the request from its distributed architecture toget the appropriate forecast information for the drone and supportingsystems, and transmits it to the drone and supporting systems. The cloudarchitecture interfaces with a GNSS Forecast Engine that gatherssatellite data, environmental data, and maps to process with variousalgorithms to provide a prediction and then publishes the prediction ona cloud architecture. The drone is then able to determine the GNSSsignal quality along its route and determine if the route can be flownsafely.

In another aviation use case, GNSS Forecasts can be applied to flightplanning and operation. GNSS is typically the primary or only way foraircraft to determine their global position. In order for the aircraftto navigate, an aircraft may use GNSS to comply to performance-basednavigation requirements, report its position using Remote ID or ADS-B,communicate position as a part of air traffic control or unmannedtraffic management, detect and avoid other aircraft, avoid obstacles,take-off, land, and even have stable flight. Therefore, it is criticalfor aircraft to predict the performance of GNSS when planning for aflight and use the same information in flight when making real-timedecisions. For manned aircraft, Wide Area Augmentation System (WAAS) andRAIM (Receiver Autonomous Integrity Monitoring) can be used for many ofthese needs, but that assumes the GNSS receiver has an unobstructed viewof the sky with no impacts of single obscuration or multipath. Moreover,these techniques may not meet the accuracy needed for an Unmanned AerialSystem (UAS) or Urban Advanced Air Mobility (AAM) system or begeographically specific enough. Moreover, while these techniques improvethe performance of GNSS, none of them provide information about thelocalized environmental impacts on the GNSS signals from buildings,vegetation, terrain, and other sources of obstructions and multipath.

Unmanned Traffic Management (UTM), including the Unmanned ServiceSupplier, can use the disclosed GNSS Forecast as a part of determining aroute, the drone's total system error, performance monitoring offlights, how it allocates airspace, route authentication and routesurveillance. UTM can also use the forecast in real-time to monitor theperformance of its navigation system and use the forecast for anyreal-time changes in flight paths. For example, if a change of flightpath is made, the UTM could ensure it does not fly into an area thatwould degrade its ability to fly.

The disclosed GNSS Forecast can also be provided as a Supplemental DataService Provider (SDSP) as a part of an UTM.

The disclosed GNSS Forecast can also be used to determine the navigationcapabilities and requirements of the airspace. For example, a GNSSForecast can be used to dynamically create geofenced areas and ensuretheir enforceability. A GNSS Forecast can be determined to determinewhat type of RNAV/RNP requirements are applicable for a flight. A GNSSForecast can be used to create static or dynamic flight corridors.

The disclosed GNSS Forecast can be used to select locations for, flightpaths to/from, and certify vertiports for electric vertical takeoff andlanding (eVTOL) and Advanced Air Mobility aircraft/services.

The disclosed GNSS Forecast can be used to determine areas ofconsistently high or low risk over long time periods. For example, aGNSS Forecast could be done over a month-long period to determine areaswhere GNSS signals are available 99.9999% of the time. These areas couldbe classified as low risk for flights. Whereas areas that do not meetthose criteria could be classified as dynamic, high risk, and thereforeeither flights are not allowed in those areas, or a discreet forecast isneeded for the exact time and location of the flight to determine GNSSperformance.

The disclosed Forecast Assured Navigation (FAN) technology also includesthe methodology of collecting and recording for playback networkconnectivity as a function of location, altitude and flight envelope,via a GNSS signal record and playback system such as Spirent GS S6400product. Network Connectivity is the primary or only way for UnmannedAircraft (UA) or Unmanned Aircraft System (UAS) to report their globalposition. In order for the UA to navigate, comply to performance-basednavigation requirements, report its position (using Remote ID or ADS-B),communicating position as a part of air traffic control or unmannedtraffic management, correctly gather data, deliver packages, avoid otheraircraft, avoid obstacles, take-off, land, and even have stable flight.An aircraft may use Network Connectivity to communicate to USS (UASService Supplier), UTM (UAS Traffic Management), and Operator and alsobe used for command and control (C2) especially when operated BVLOS(Beyond Visual Line of Sight). Therefore, it is critical for aircraft toknow in advance the performance of Network Connectivity when planningfor a flight. For terrestrial vehicles, the cell tower antennas aredesigned to ensure adequate ground coverage. This is not the case as aUAS gains altitude and may be operating off of a side lobe, resulting inover-propagation and having many more interference effects.

An aircraft mission/flight planning system can use a NetworkConnectivity Record and Playback application to ensure that adequateconnectivity is available for a flight, type of operation, type ofairspace, and type of aircraft. Moreover, an UAS Traffic Management(UTM) system can use a Network Connectivity Record and Playback as apart of determining total system error and performance monitoring offlights and how it allocates routes and airspace. When a flight path ismade the UAS could ensure it does not fly into an area that woulddegrade its ability to fly, perform, or meet the requirements of theairspace. Network Connectivity Record and Playback can also be used todynamically create geofenced areas and ensure their enforceability.

In one case, technology for record and playback network connectivity canbe used for part 135 certification. Another use case includes theapplication of record and playback network connectivity for UA routeapproval. A third case includes the application of record and playbacknetwork connectivity for UA type certification. Another case utilizesthe application of record and playback network connectivity forvertiport certification. Yet another case includes the application ofrecord and playback network connectivity for remote ID certification.Also included are the disclosed application of record and playbacknetwork connectivity for receiver/antenna selection, and the applicationof record and playback network connectivity for forecasting modelvalidation.

Broadband Service Coverage Over Time

For visualizing broadband service coverage over time, one implementationof a disclosed method includes providing for display an interface thataccepts input, and receiving the input specifying a point location for abroadband service antenna and visualization parameters, including a timerange to depict. The method also includes accessing a 3D map of an areain an urban environment surrounding the location, including structuresolids that obscure lines of sight from the location, and computingrequested visualization over time, including ray casting or tracingbetween point location and satellites over the time range. Also includedis initializing a 3D map visualization, overlaying the point location onthe 3D map for a selected time and overlaying first and second rays fromthe point location towards at least some satellites overhead at theselected time, visually encoding the first rays with a first code toindicate a clear line of sight and overlay the encoded first rays on the3D map, and visually encoding the second rays with a second code toindicate a blocked line of sight, showing at least a segment from thepoint location to a face of a blocking solid that blocks a line ofsight. The disclosed method further includes combining the 3D mapvisualization with a playback animation control for the time range,including a current time represented by the overlaid first and secondrays and third segments, overlaying the playback animation control withan indication of line of sight visibility at the point location over thetime range, and providing for display the 3D map visualization combinedwith the playback animation control, including the overlaid first andsecond rays and third segments and the overlaid indication of line ofsight visibility. The received input specifies satelliteconstellation(s) to depict. The method includes ray casting or tracingbetween additional locations, along a surface of a structure on whichthe broadband service antenna is mounted, and overlaying third segmentsalong the surface of the structure and visually encoding the thirdsegments with a third code to indicate lines of sight availability alongthe additional locations on the third segments. The method can includeshowing the segment extending a second ray skyward from a far side ofthe blocking solid. In some cases, the method also includes ray castingor tracing between additional locations, on at least part of a face of astructure on which the broadband service antenna is mounted, andoverlaying visual encoding of the face of the structure with a thirdcode to indicate lines of sight availability along the additionallocations on the face.

Visualizations that illustrate route planning and evaluation examplesthat utilize DOP forecasts for GNSS navigation are described next.

Visualizations

As an alternative to distributing forecast data to and via the contentdelivery network (CDN) service 158, the data can be used to renderdisclosed visualizations of the forecast. GNSS signals change in urbanenvironments, as is viewable via a map, by altitude and over time. Userscan search for a route with “good” GNSS coverage, using the disclosedGNSS Forecasts described herein. Users can visualize GNSS performancearound a known route and adjust if needed.

FIG. 7A illustrates one example of the disclosed Forecast AssuredNavigation (FAN) user interface (UI). A browser-based UI operator's viewof the system is driven by the FAN data retrieved from the schedulercuration service 364, map curation service (MCS) 384 and logging andevent service (LES) 372. FAN UI 702 shows a snapshot in time thatillustrates where buildings occlude line of sight, via the lines thatshift from an alternating dot and dash pattern that shows LOS to longdashed lines that show NLOS, with PDOP 3.7 at the receiver 755. LOSsatellite signals are depicted with an alternating dot and dash pattern752 and NLOS satellite signals are depicted using long dashes 732. Asignal coming from a satellite in the sky is depicted as LOS before thesignal encounters a building that blocks the signal. The UI displays theshift to NLOS long dashes pattern between the building and the receiver755, as shown in FIG. 7A. The disclosed FAN user interface 702 alsodepicts a count of satellites that are visible over time. Satellitecount 772 is graphed over time with the alternating dot and dash patternfor higher counts of satellites visible, and with the long dash patternto illustrate fewer visible satellites at other times along thetimeline. FAN user interface 702 further depicts a graph of DOP changesover time 782 for the receiver, via the disclosed timeline visualizationshown near the bottom of the UI. A point in time can also be selected toview the PDOP, visibility and obscuration 786. Higher PDOP values aredepicted with an alternating dot and dash pattern and lower PDOP valuesare depicted using long dashes, in this example implementation. Thedisclosed FAN UI can utilize a variety of graphs and patterns in anotherimplementation. For example, forecast maps 704 and forecastsubscriptions 708 can be accessed via UI 702.

Users can access the forecast data via a visualization in a UX, and canchange perspective, using rotate, pan and zoom features. A PDOP 2D layerdisplays the heatmap at a given altitude as a heatmap slice and userscan utilize a slider on the UX to move the layer up and down inaltitude. Additionally disclosed, users can view the data in 3D spaceusing a PDOP 3D heat cloud that shows the heatmap in 3D, and thetimeline can display the PDOP changing over time and illustrate how timeimpacts the heatmap.

In addition to PDOP, geometrical DOP, horizontal DOP, vertical DOP andtime DOP can be computed and displayed in the forecast data, via the FANUI. These values can be computed for a combination of satellitenavigation systems, i.e., as a “multi-GNSS DOP”, which refers to thecombination of more than one satellite system. The values canalternatively be provided for each individual constellation, includingGPS, GLONASS, Galileo and BeiDou, for example. In addition to DOP, otheritems which can be calculated and displayed include the number ofvisible LOS satellites and the number of not visible NLOS satellites.The level of multipath in an area can be estimated or mathematicallyanalyzed, including general level of multipath in an area, level ofmultipath on a multi-GNSS constellation basis, level of multipath on aper GNSS constellation basis, and level of multipath on a per satellitebasis. Predicted position errors and relative predicted position errorscan also be calculated and displayed. As well as computing the values,the display of these can also be altered. For example, each value, ortype of value can use its own, individual color palette and way ofdisplay. This can be automated, or manually chosen. The display elementscan be context sensitive, depending upon the type of question that isbeing asked in the request. The displayed items can be focused torespond to particular issues, such as worst area, best area, relativeperformance spatially in 2D, vertically in 3D, and temporally to showchanges over time.

FIG. 7B illustrates the relationship between accuracy and precision fortarget satellite locations, for visualizing and evaluating navigationcorridors. When a satellite location is accurate and the precision ishigh, as shown in (a) 742, there is less uncertainty in the location ofthe vehicle. Alternatively, when the accuracy is low and the precisionis low as shown in (d) 748, a forecast trajectory would be lessreliable, which can be indicated by showing a wide corridor.

GNSS Forecast can be utilized to predict where and when satellitenavigation meets the technical requirements for a specific use case. Inone example, real-time integrity improvement of a navigation sub-systemis achieved via real-time use of the GNSS Forecast to look approximately1 KM ahead of the vehicle to determine what GNSS is available, whatprecision is possible, if mitigation is sufficient, and what level ofautonomy can be maintained. Then, if needed, the system can changeautonomous mode or provide additional time for disengagement to thedriver.

FIG. 8A illustrates the effect of turning a geometric corner in an urbanenvironment on the number of LOS satellites within a target area.Turning the corner can immediately change GNSS coverage quality. Forexample, for a receiver traveling West to East, with buildings depictedas shaded areas in the drawing, six satellites are LOS 824, as depictedby +s in the white target area of the path. After the receiver turns thecorner to travel North to South, only three satellites are LOS 866 inthe white target area of the path in the urban environment shown. Thedisclosed FAN enables the receiver to know in advance to expect to usedifferent satellites after turning the corner in an urban canyon. As anexample of the effects illustrated in FIG. 8A, a GNSS Forecast canpredict the LOS satellites. However, a specific receiver and antenna maynot track all of the satellites due to their design and performance. Inone example, an implementation may filter out satellites with a specificsignal to noise ratio. Characterizing the way each receiver chooses totrack satellites compared to the predicted satellites enables aperformance prediction for specific receivers.

The disclosed Forecast Assured Navigation (FAN) Forecasts can also beapplied to improve vehicle-to-vehicle communication (V2X). Criticalroadway infrastructure such as smart intersections are often tasked withadvanced traffic flow management to optimize timing of traffic lightsand decrease congestion. Lane level accurate vehicle position istherefore provided by the vehicle to the traffic management systeminfrastructure (V2X). The FAN Forecast can improve the integrity of thevehicle's position, such as in a turn lane, and therefore improve thesystem's performance.

FIG. 8B shows an example urban roadway for utilizing FAN Forecasts for asmart intersection, illustrating the need for accurate GNSS Forecasts,pictorially. Vehicle 844 can use the disclosed real-time GNSS Forecastthat considers the number of LOS satellites, to improve measurement andpositioning estimation.

GNSS forecasts can also be applied for city infrastructure planning. Theability of GNSS Forecast to predict GNSS signals' integrity could beused to plan city infrastructure. For the city in situ with allinfrastructure in place, GNSS Forecast can be used to work out areaswhere GNSS signals suffer from impairments. This information can then beused to identify the locations for installing relevant sensors in orderto mitigate these adverse effects. If the user has the ability to updatethe 3D map used in a GNSS Forecast, it can be used for cityinfrastructure planning during re-development. GNSS Forecast can be usedto predict the effects of the new infrastructure on GNSS signalsintegrity and design the infrastructure for optimum GNSS signalsintegrity in the scenarios listed next: (a) Design potential GNSSinterference sources to be interoperable with GNSS capabilities; (b)Design infrastructure to have a lower number of disturbances to areaswhich are relying on GNSS signals to function; (c) Design theinfrastructure for vehicles to travel in optimum GNSS integrity; (d)Find locations at which to install relevant sensors, for situations inwhich GNSS signal degradations due to infrastructure are unavoidable.

Starting Location

Two further approaches not only employ satellite visibility in the blockfor a given estimated starting position, but also the adjacent blocks,to more obtain the starting position. The further approaches areillustrated in FIGS. 8C, D.

FIG. 8C illustrates the environment of fast convergence for low initialuncertainty, illustrated in an urban environment, and a first approachto efficient startup convergence.

Diagram 800 a illustrates an east-west street, with blocked satellite804 and visible satellites 807, 818, obstructions 813, 816, 833,location 821 with a vehicle carrying a receiver, and adjacentlocation(s) 825.

Blocked satellite 804 has a signal that is blocked in at least one oflocation 821 or adjacent location(s) 825 (here, the forecast datapredicts that it is visible to the receiver at location 821 because theangle permits the signal to travel over obstruction 813, but will not beat adjacent location 825 because the angle is blocked by obstruction816.)

The visible satellites have a signal that can reach location 821 andadjacent location(s) 825. Specifically, visible satellite 807 is at anangle to have non-LOS (NLOS) visibility at location 821 because itcrosses atop obstruction 816 and the signal is reflected off ofobstruction 833 before reaching location 821, and LOS visibility at 825.Visible satellite 818 has LOS with the vehicle at location 821 andadjacent location 825 because it is not blocked by any obstruction ateither location.

Analyzing the GNSS forecast data provides lists satellites that arevisible in the locations. The analysis is collated to find satellitesthat are common to all of the locations 821 and 825, including boththose with LOS and with NLOS. Here, visible satellites 807, 818 arevisible in both location 821 and adjacent location 825. Blockedsatellite 804 is precluded from the final result because it is blockedfrom visibility at adjacent location 825, despite its visibility atlocation 821.

By taking the list of satellites visible to the receiver, and comparingthat with the lists of satellites that are visible in the adjacentlocations, one can determine which location corresponds to thereceiver's location. In urban environments, this may around 20-30seconds. In suburban or rural areas, convergence may occur more quickly.Two approaches to using this information follows.

Diagram 800 b illustrates the first approach to initial startupconvergence by buttressing an initial convergence with GNSS forecastdata. Diagram 800 b shows an intersection of streets and positionuncertainties regions 842, 844, and 846 at respective times t₀ throught₂, and satellite group 1 841, group 2 832, and group 3 843.

Uncertainty regions 842, 844, 846 are depicted as a circle, but in someimplementations, may instead be an ellipse. The depicted uncertaintyregions 842, 844, and 846 may have a confidence level at or above 95%,but in practice, the initial confidence interval may range to extremelylow values, such as 50%.

The various satellite groups are only shown at to t₀ reduce diagramclutter; they are present at all times in the following discussion.

Satellite group 1 841 represents the group of satellites that have LOSto all valid observable points in uncertainty region 842. Those regionsthat fall within the middle of terrain or a building in 842 are notconsidered for membership into satellite group 1 841.

Satellite group 2 832 represents the group of satellites that have LOSto the valid observable points along the north-south path.

Satellite group 3 843 represents the group of satellites that have LOSto the valid observable points along the east-west path.

At t₀, with an imprecise initial starting position, the various visiblesatellites in satellite group 1 841, satellite group 2 832, andsatellite group 3 843 were used to determine uncertainty region 842.Although the centroid of the initial startup position is in the middleof the intersection, the location could be anywhere within the circle socould be on the north-south street or east-west street outside of theintersecting area. In the example, uncertainty region 842 could expresspotential locations within 50 meters of the circle's center. Thereceiver could be on the north-south path, or the east-west path, orboth (i.e. the receiver could be in the middle of the intersection).

Blocks of predictive data of not only the imprecise initial startinglocation, but also of locations that adjoin the initial startinglocation, such as locations that fall in uncertainty region 842, areobtained from a forecast data source such as Content Delivery Network(CDN).

To obtain a first convergence of locations, satellites that are known tohave LOS visibility to all locations in uncertainty region 842 areweighted higher than those without, and thus the accuracy for a firstconverged starting position should be higher than the imprecise initialstarting location. In the example, when refining the assessment of thestarting position to obtain a first starting position, satellites insatellite group 1 841 are weighted comparatively higher to thosesatellites in satellite group 2 832 and satellite group 3 843. In someimplementations, satellites that are not commonly available are ignored.

The position has converged at t₁ to a first staring position, reflectedby uncertainty region 844. In some implementations, the process may stophere, either because the process is being used as a parallel sanitycheck on initial starting location process, or because the firstconvergence provides a sufficiently accurate position. However, in manyimplementations, the first convergence will not suffice, and one or moreadditional iterations can be used to obtain even further precision.

Uncertainty region 844, obtained by weighting the contribution ofsatellite group 1 841 over those of satellite group 2 832 and satellitegroup 3 843, is smaller than uncertainty region 842 and uncertaintyregion 844 falls on the north-south path rather than the east-west path.There are more commonly visible satellite with LOS to all VOPs inuncertain region 844 than there were in uncertain region 842.

In the example, not only do the satellites in satellite group 1 841 haveLOS visibility to all locations in uncertainty region 844, thesatellites in satellite group 2 832 also have LOS visibility to alllocations in uncertainty region 844. Thus, to perform a further secondconvergence to result in a second staring position, satellites from bothsatellite group 1 841 and satellite group 2 832 are weighted higher thanthose of satellite group 3 843.

At t₂, weighting satellites know to have LOS visibility over those thatmay have NLOS visibility (namely, those in satellite group 1 841 andsatellite group 2 832) results in even better accuracy of positioning,reflected by the even smaller uncertainty region 846. If the secondstarting position is sufficiently precise, the process may stop at thispoint. Otherwise, the process may continue with further iterations totn.

In conditions of where the receiver is clear of most obstructions, finalconvergence may occur in under 20 or 30 seconds.

FIG. 8D illustrates a second approach to convergence by correlatingsatellite visibility data with signal strength by the receiver. Diagram800 c includes candidate locations 812, 831, and 841, and visiblesatellites 802, 803.

Candidate locations 812, 831, 841 are Valid Observable Points (VOPs)that represented starting locations that could be the initial startinglocation. These VOPs fall within an uncertainty region having of 95%confidence. Darker candidates are candidate locations above a threshold.Visible satellites 802, 803 are satellites that are predicted to bevisible to candidate locations 812, 831, 841.

The second approach is based on SNR pattern matching. Since the receiverhas the GNSS forecast data for nearby VOPs, the receiver can derive, foreach VOP, a projected SNR profile for the visible satellites. An SNRprofile for the GNSS satellite signals detected as the receiver is alsocreated as a detected SNR profile. This profile is correlated tocandidate points to find the candidate

In perfect conditions, with detected SNR profile and the plurality ofprojected SNR profiles in hand, a single candidate with a projected SNRprofile would stand out has having far and away the best likelihood tomatch the detected SNR profile. That candidate position couldimmediately be reported as the initial staring location.

However, it is rare to have perfect conditions. Firstly, it is likelythat several of the candidates have a high degree of correlation to thedetected signal profile. Diagram 800 c shows candidates 812, 831, 841 asdarkened to reflect a high likelihood of the actual starting location.Secondly, at any point in time, real world satellite signals may bediffer from the prediction based on transient factors that are notreflected in the GNSS forecast data (e.g. atmospheric or weathereffects, large moving trucks or low flying birds temporarilyinterrupting a signal, and other sources of uncertainty as understood bya skilled practitioner.)

To adjust for uncertainty, the technology employs a Kalman filter thatreflects a received signal strength (RSS) from each satellite as a statematrix, and that reflects the uncertainty of the satellite signals andthe correlation of signals between each pair of satellites (based on thepredictive SNR profile generated from the GNSS forecast data) as aprocess covariance matrix. Visible satellites (both LOS and NLOS) areused to create these matrices.

Both the state and process covariance matrices are used, with GNSSforecast data, to predict signal strengths of each individual satelliteand uncertainty at a later time. The later time may be within a fewseconds, or fractions of seconds.

At the later time, receiver observes the signals from each satellite anddetermines measurement noise. The receiver compares the prediction ofwith measurement, and obtains a weight matrix (aka the Kalman gain).This weight matrix is then used with the differences between thepredicted state and the measured state to update the state matrix.

Once updated, the state matrix is used to update the probabilitiesrelated to the candidate locations.

Returning to the example in FIG. 8D, when comparing candidates 812 and841 as counter-examples to candidate 831, it can be seen that candidate831 corresponds to a VOP such that satellites aligned with LOS along theeast-west street should have LOS. If the receiver's SNR profile isconsistent with only north-south satellites having LOS and east-westsatellites only have NLOS visibility, the detected SNR profile suggeststhat the probability of the initial starting location should be adjustedin favor of candidates 812 and 841.

If, after the probabilities are updated, if a threshold requirement ismet that, based on the Kalman filter output and the plurality ofpredicted SNR profiles, that one candidate location is likely to beinitial staring location, then that candidate location is considered tobe the initial starting location and the startup processing to determinethe initial location ends.

On the other hand, if the threshold requirement is not met, thenconvergence process continues. The process covariance matrix is updatedusing the weight matrix. Once the process covariance matrix is updated,the cycle repeats. In other words, state matrix and the processcovariance matrix are used to predict new states at a later time,satellite measurements are sampled at that later time, etc.

Although the process is not guaranteed to converge to an initialstarting location, in most situations, the process converges withinseconds.

The determinations required by either the first or second approach mayinvolve at least some calculation at the receiver. In someimplementations, the calculations may be performed entirely by thereceiver. The two approaches may be used severally or in parallel.

FIG. 9A through FIG. 9F illustrate use of the disclosed visualization UXthat enables route planning and evaluation by users. In one use case,the customer is searching for a route that has sufficient GNSSperformance for safe navigation when operating within the receiver'sdesign domain, using GNSS Forecast data. The customer starts with the3D/2D map with the PDOP layer enabled, to view locations with good/badGNSS coverage on the map. The customer can then drop pins to define aroute that meets their requirements for GNSS performance. In the exampleillustrated in FIG. 9A through FIG. 9F, a user enters a route to checkwhat the GNSS performance for the route is, and to adjust the route, ifneeded, using the GNSS Forecast data displayed as a heat map.

FIG. 9A illustrates the “create a route” feature with entry of a routeby selecting the route button or by importing of a Keyhole MarkupLanguage (KML) file, an XML notation for expressing geographicannotation and visualization. The interface accepts and receives inputspecifying an urban area for route planning and visualizationparameters, including a time range and optionally an elevation range andsatellite constellation(s) to depict.

FIG. 9B shows the completed route 926, before the user selects thefinish feature to display route information. The solid line illustratesthe intended path/trajectory. The individual points are where thevehicle/receiver is located, and the window is the expected errorallowance. The wider the window, the more uncertainty. If theuncertainty gets large then the “bubble” will be big and will intersectwith buildings, making that path unfeasible.

FIG. 9C displays the enabled PDOP layer with the GNSS forecast heat map.The route length is displayed as 1.73 km 944. Shading visually encodesline of sight visibility or dilution of precision at the point locationover times and combines the 2D map visualization with a scrubbing slider974 to control a time represented by the overlaid first and second raysand third segments, optionally overlaying the scrubbing slider control974 with an aggregate indication of line-of-sight visibility or dilutionof precision in the urban area for route planning, over times in a rangespanned by the scrubbing slider.

FIG. 9D shows the visualization interface with the DOP layer feature on,in 2D side view 932, overlaying shading on the 2D map for a selectedtime. FIG. 9E shows the route map 936 as it is entered, and the flightcorridor is calculated and displayed alongside the route, in both topview 938 and side view 948.

FIG. 9F shows the completed route 964 with the GNSS forecast datainforming the route via the displayed heat map in which different DOPranges are displayed in different shadings. Consideration of DOP rangeshelps to avoid degraded performance and ensure the tightest possibleflight corridor.

FIG. 9G shows another view of the visualization interface that enablesusers to plan a route by clicking on the map while the heat map isvisible. The thin dotted line around the route shows the estimatedflight corridor. The “bubble” around the corridor implies that thereceiver will be anywhere in that position even though the path isillustrated in the dead center. That is, a wider corridor implies lessaccuracy, and therefore more uncertainty, and a narrow corridorsignifies more accuracy, and therefore less uncertainty. For aviation,the customer can look at a top/side split view to adjust the altitudesof each segment of the flight, accessing a 3D map of the urban area,including structure solids that obscure lines of sight between cuboidsin the area, and optionally for drones in the specified elevation range,and satellites overhead.

FIG. 10A shows a 3D visualization of the forecast interface displayingthe GNSS Forecast DOP in measure mode, for a location selected by theuser. The DOP values can also change over time on the timeline displayedat the bottom of the 3D UI display when the reticule 1024 is movedaround on the map by a user.

FIG. 10B shows rays enabled 1066 for viewing individual lines of sightfrom reticule to satellites in orbit. Scrubbing slider 1086 can controla time represented by the overlaid first and second rays and thirdsegments, optionally overlaying the scrubbing slider control 1086 withan aggregate indication of line-of-sight visibility or dilution ofprecision in the urban area for route planning, over times in a rangespanned by the scrubbing slider. The timeline 1084 can be scrubbed toview the predicted PDOP at a particular time.

FIG. 11 , FIG. 12 and FIG. 13 illustrate a GNSS Forecast visualizationfor multiple instances of a 1 km×1 km grid selected to cover the centerof the city of Indianapolis, at different sample times, and at multipleelevations. Six shadings encode six different DOP value bands in thevisualizations.

FIG. 11 displays the GNSS Forecast at 20:23:00 GPS at ground level. Notethat navigation in the city center 1156 is impacted, as illustrated bythe high DOP values.

FIG. 12 displays the GNSS Forecast at 20:47:18 GPS at ground level. Notethat navigation in the city center is less impacted only 24 minuteslater, as illustrated by the lower DOP values shown in the visualizationin the city center 1256.

FIG. 13 displays the GNSS Forecast at 20:47:18 GPS at 30 meters aboveground level. Note that the graph of DOP value bands shows low DOPvalues in the downtown area 1356, which is accessible at the height of30 meters above ground level.

GNSS Forecast maps show GNSS receiver calculation position compared toGNSS actual position. For multi-constellation receiver and forecast(with RTK) tests performed with the disclosed GPU-based system usingcommercial 3D mapping, initial performance results show 94-98 percentcorrelation with real world measurements. GNSS performance bands showdistinctions between ideal, excellent, ok, moderate, poor and badenvironments. Forecast predictions match real world performance, withpoor and bad predictions corresponding to poor and bad positioningaccuracy. The Forecast Assured Navigation (FAN) Forecast can be utilizedfor accurate prediction of GNSS-degraded environments.

FIG. 14 , FIG. 15A, FIG. 5B and FIG. 16 illustrate GNSS Forecastvisualizations at three sequential times, separated by 12 minutes each.FIG. 14 depicts a route along West Ohio Street in the urban center ofIndianapolis that shows where the receiver thinks it is, at 12 minutesbefore the mid-point time of the drive 1424, encoded with PDOP valuebands. A second depiction shows the route at the mid-point time of thedrive 1434, and a third depiction shows the PDOP along same route 12minutes later in time for the mid-point of the drive 1454 along WestOhio Street. Path 1438 is the route that the receiver actuallytravelled, straight from left to right along the route. The threecircled areas 1414, 1444, 1464 of FIG. 14 illustrate the correlationbetween the prediction via PDOP values and the receiver's performance.The PDOP values predict the degradation of the receiver's signals. Thereceiver's performance recovers when the PDOP value bands return tolower values 1448 during the mid-point of the drive 1454 along West OhioStreet. Disclosed FAN forecast predictions enable avoidance of pitfalls,by planning a different route, time, or altitude. The best route in/outof the area can be evaluated. Route issues can be mitigated, using thepredictions to determine whether the user can operate without GNSS andfor what time period.

FIG. 15 depicts 3D GNSS Forecast visualization results for West OhioStreet, Indianapolis, at four different altitude planes. The heat mapsfor ground level 1552, +10 m 1542, +20 m 1532 and +30 m 1522 show therelative PDOP graphs for those four altitudes. The area with high PDOPvalue band is larger at ground level, and gets smaller as the altitudeincreases. This supports the expectation of a lower DOP and a resultinghigher accuracy of signal when the altitude rises above the level of thebuildings in the urban landscape.

FIG. 16 depicts floating planes of signal strength for visualizingsignal coverage over time. FIG. 16 shows a lateral view of 3D GNSSForecast visualization results for West Ohio Street, Indianapolis, atfour different altitude planes, with the four paths stacked at the samealtitudes as those shown in FIGS. 15A and 15B. The rotation ofperspective shows the effect of buildings in the urban corridor on PDOPvalues and the resulting receiver performance. At higher altitudes, theareas of degraded performance become smaller but do not disappear. FIG.16 offers a sense of where the planes for the route are located relativeto the height of the buildings.

In one implementation, with floating planes of signal strength, adisclosed method of visualizing GNSS coverage over time for routeplanning, includes providing for display an interface that acceptsinput, receiving the input specifying at least one corridor through anurban area being traversed and visualization parameters, includingelevation slices and a time range, and in some cases a flight planthrough the corridor and satellite constellation(s), to depict. Themethod also includes accessing a 3D map of the urban area, includingstructure solids that block lines of sight between cuboids on theelevation slices along the corridor and satellites overhead, wherein theelevation slices are bounded planes in space between the structuresolids and are parallel to a surface of the 3D map, and computing arequested visualization over time, including ray casting or tracingbetween the cuboids and satellites over time to calculate line of sightvisibility or dilution of precision at the point location over the timerange. The method further includes initializing a 3D map visualization,overlaying the elevation slices on an orthogonal projection from aviewpoint above a lowest elevation slice and below a highest elevationslice, wherein each elevation slice translucently encodes theline-of-sight visibility or dilution of precision for the cuboids on theelevation slice. In some implementations, the method includes overlayingflight plan segments through the corridor on the orthogonal projectionand visually encoding the segments to indicate the line-of-sightvisibility or dilution of precision along the route segments. The methodalso includes combining the 3D map visualization with a scrubbing sliderto control a departure or arrival time depicted, and in some casesoverlaying the scrubbing slider control with an indication ofline-of-sight visibility or dilution of precision over the flight planthrough the corridor at times in a range spanned by the scrubbingslider. The method further includes providing for display the 3D mapvisualization combined with the scrubbing slider control, including theoverlaid elevation slices.

For visualizing signal strength via translucent clouds, in oneimplementation a disclosed method of visualizing GNSS coverage over timefor flight planning includes providing for display an interface thataccepts input, and receiving the input specifying at least one corridorthrough an urban area being traversed and visualization parameters,including an elevation range and a time range and in some cases a flightplan through the corridor and satellite constellation(s) to depict. Thedisclosed method also includes accessing a 3D map of the urban area,including structure solids that block lines of sight between cuboids inthe elevation range along the corridor and satellites overhead, whereinthe corridor and elevation range occupy space between the structuresolids of the 3D map, and computing a requested visualization over time,including ray casting or tracing between the cuboids and satellites overtime to calculate line of sight visibility or dilution of precision atthe point location over the time range. The method further includesinitializing a 3D map visualization overlaying a translucent cloud ofvalues of points, of equal value surfaces on an orthogonal projection,wherein the values encode the line of sight visibility or dilution ofprecision for the cuboids in the translucent cloud. In someimplementations, the method also includes overlaying flight plansegments on the orthogonal projection and visually encoding the segmentsto indicate the line of sight visibility or dilution of precision alongthe flight plan segments. The disclosed method also includes combiningthe 3D map visualization with a scrubbing slider to control a departureor arrival time depicted, and can include overlaying the scrubbingslider control with an indication of line of sight visibility ordilution of precision over a route through the translucent cloud attimes in a range spanned by the scrubbing slider. The method furtherincludes providing for display the 3D map visualization combined withthe scrubbing slider control, including the overlaid elevation slices.

FIG. 17 illustrates an example customer request with a four-pointpolygon 1756, 1766 and min/max heights. The pinned planes show the areacovered by a map tile split into cuboids. A customer can pass throughthe area and request and receive back the set of GNSS Forecast cuboidsfor that area.

In an additional use case, an API view enables users to visualize GNSSForecast data requested and received using the APIs. The developer canmove the reticule around and view a dynamically updated display of theresults of example API calls. Developers can also adjust API time windowvalues via controls on the timeline, and can view a live response fromthe APIs to help debug calls to the APIs.

Simulation-Based Implementation

An implementation of a GNSS positioning assurance application has beendeveloped for predicting and visualizing the influence of satellitegeometry on the GNSS position error across a series of points in a givengeospatial region. “Appendix A: Simulation-Based Trial Implementation”which is included in full herein for reference, provides a fulldescription of the Simulation Implementation, with user interface,architectural overview, simulation orchestrator threading and simulationorchestrator. Appendix A also includes the UI and User Manual forSimulation-Based Implementation. The development of this simulationapplication was driven by the requirements for assured mission-planningfor the autonomous vehicle industry. The disclosed application includesparallel execution of simulations allowing the simultaneous testing ofmultiple points, the ability for area and route assessments to beprovided, and a high level of scalability, resulting in a morecomprehensive test than existing mission-planning solutions. Thecompleted application leverages up-to-date APIs and ray-casting toapproximate signal obscuration caused by nearby structures and can alsobe used to highlight potential problem areas in mission routes. Theapplication has been developed as a solution that can provide assuranceto manufacturers, that the viability of areas and routes can bepredicted before a journey is made. The application focuses on GPSsignals and can inform users of the coverage quality of a given testscenario ahead of time. The applicant's proprietary software, SimGEN,usable to simulate the predicted orbits of GPS satellites, is used forreceiver testing. The disclosed application harnesses multiplesimulations to perform area tests for highlighting potential problemareas for CAV missions and routes.

The preceding description is presented to enable the making and use ofthe technology disclosed. Various modifications to the disclosedimplementations will be apparent, and the general principles definedherein may be applied to other implementations and applications withoutdeparting from the spirit and scope of the technology disclosed. Thus,the technology disclosed is not intended to be limited to theimplementations shown but is to be accorded the widest scope consistentwith the principles and features disclosed herein. The scope of thetechnology disclosed is defined by the appended claims.

Some Particular Implementations

We describe some particular implementations and features usable forproviding dilution of precision (DOP) forecasts and/or degree ofconfidence forecasts for GNSS navigation next.

We describe some particular implementations related to the use of GNSSforecasts.

Clause Set 1

One implementation of a disclosed method of reducing starting time for aGlobal Navigation Satellite System (GNSS) receiver that has an impreciseinitial starting location includes requesting starting assistance, bythe GNSS receiver, from a Content Delivery Network (CDN) that cachespredictive data including first data indicated predicted LOS visibilityfrom the receiver to individual satellites, wherein the request includesthe imprecise initial staring location. The method further includesreceiving, from the CDN, data that includes a first block of thepredictive data for the imprecise initial staring location and furtheradjoining second blocks of predictive data for areas surrounding theimprecise staring location. The method also further includes a firstiteration, where the iteration determines, by the GNSS receiver,commonly available satellites that have visibility from locations inboth the first block and the second block, and calculates a firststarting position using weighted values for the satellites, the commonlyavailable satellites having higher weighted value than satelliteswithout visibility in both locations, whereby position uncertainty ofthe first starting position is reduced from the imprecise initialstarting location.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

For some implementations, the method further includes a seconditeration, where the second iteration determines, by the GNSS receiver,additional commonly available satellites that have visibility from bothlocations in the first block and locations in the second block andcalculates a second starting position using updated weighted values forthe satellites, the commonly available satellites and additionalcommonly available satellites having higher weighted value thansatellites without visibility in both locations, whereby positionuncertainty of the second starting position is reduced from the firststarting position. Some of those iterations may include additionaliterations including third, fourth, and more.

For some implementations, the method further includes the imprecisestaring location and the areas surrounding the starting location havingno common satellites, waiting for a next position fix, and repeating thesteps of receiving, determining, and choosing.

For some implementations, the method further includes determiningsatellites that are not commonly available in the first block and thefurther adjoining blocks, and ignoring satellites that are not commonlyavailable.

For some implementations, the method further includes the adjoiningblocks are based on an initial uncertainty of the initial position. Insome implementations, the initial uncertainty of the initial position isan error ellipse.

For some implementations, the determining occurs by collating theadjoining blocks.

For some implementations, the predictive data includes second dataindicating predicted NLOS visibility from the receiver to the individualsatellites.

One method of providing a fast and accurate convergence during startupincludes beginning at an initial location with an initial locationestimate, whereby the initial location estimate is inaccurate. Themethod also includes requesting GNSS forecast data about cuboids in ageographic volume including the initial location estimate. The methodalso includes receiving the GNSS forecast data for the geographic volumeand extracting predicted satellite visibility from the GNSS forecastdata for candidate locations proximate to the initial location estimate.The method also includes ascertaining, for each candidate location, apredicted signal to noise ratio (“predicted SNR”) based on the predictedsatellite visibility. The method also includes receiving GNSS satellitesignals and, based on the received GNSS satellite signals, ascertaininga detected signal to noise ratio (“detected SNR”). The method alsoincludes comparing the predicted SNRs with the detected SNR. The methodalso includes determining, based on the comparing, a probability thatthe receiver is at the respective candidate location.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

In some implementations, the comparing step includes a Kalman filter.The method further includes generating a Kalman filter by substeps inincluding creating an input vector based on the detected SNR, creating astate matrix based on predicted SNR of each satellite and predictedrelation between each pair of satellites wherein the predictedinaccuracy and predicted relation between pairs are derived from theGNSS forecast data, creating a prediction state matrix based on theinput vector and the state matrix, obtaining a first sample of satellitedata and first measurement error, and updating the state matrix usingthe predicted state matrix and the first sample of satellite data.

In some implementations, the geographic volume does not exceed theheight of a tile.

Clause Set 2

One implementation of a disclosed method of detecting and rejecting aspoofing signal source includes receiving at a first device a forecastof a visibility for each Global Navigation Satellite System (GNSS)satellite signal source in the forecast. The method also includescalculating, at a GNSS receiver coupled to the first device, from atleast an elevation and the received visibility of the satellite signalsources in the forecast a predicted Signal to Noise Ratio (“SNR”). Themethod also includes comparing SNR acquired by the GNSS receiver of oneor more of the purported satellite signal sources to the predicted SNR.The method also includes detecting a spoofing signal source based onacquiring a higher SNR than predicted. The method also includesrejecting the spoofing signal source based on differences between theacquired and predicted SNR.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

In some implementations, the method further including receiving, in theforecast, the elevation data for the satellite signal sources in theforecast. In some implementations, the method further includesreceiving, in the forecast, azimuth data for the satellite signalsources in the forecast and using the azimuth data in the calculating.

In some implementations, the prediction of the SNR/RSS occurs at thefirst device.

In some implementations, the prediction further includes reporting thedetected spoofing, whereby other devices coupled to other receivers areinformed of the detected spoofing. In some of those implementations, thereport is sent to a central authority.

In some implementations, the predicted SNR/RSS is based on predicted LOSdata and predicted NLOS data.

In some implementations, the method includes repeating, at a later time,the actions of receiving, calculating, and comparing. Additionally, thedetecting action is based on an earlier result of the comparing action,and a later result of the repeated comparing action.

One implementation of a disclosed method of detecting and rejecting aspoofing signal source includes receiving at a first device a forecastof a visibility for each Global Navigation Satellite System (GNSS)satellite signal source in the forecast. The method also includescalculating, at a GNSS receiver coupled to the first device, from atleast an elevation and the received visibility of the satellite signalsources in the forecast a predicted Signal to Noise Ratio (“SNR”). Themethod also includes comparing SNR acquired by the GNSS receiver of oneor more of the purported satellite signal sources to the predicted SNR.The method also includes detecting a spoofing signal source based onacquiring a higher SNR than predicted. The method also includesrejecting the spoofing signal source based on differences between theacquired and predicted SNR.

One implementation of a disclosed a method of detecting and rejecting ajamming signal source includes receiving at a first device a forecast ofa visibility for each Global Navigation Satellite System (GNSS)satellite signal source in the forecast. The method also includescalculating, at a GNSS receiver coupled to the first device, from atleast an elevation and the received visibility of the satellite signalsources in the forecast a predicted Signal to Noise Ratio (“SNR”). Themethod also includes comparing SNR acquired by the GNSS receiver of oneor more of the purported satellite signal sources to the predicted SNR.The method also includes detecting a jamming signal source based onacquiring a lower SNR/RSS than predicted. The method also includesrejecting the jamming signal source based on differences between theacquired and predicted SNR/RSS.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

In some implementations, the method further including receiving, in theforecast, the elevation data for the satellite signal sources in theforecast. In some implementations, the method further includesreceiving, in the forecast, azimuth data for the satellite signalsources in the forecast and using the azimuth data in the calculating.

In some implementations, the prediction of the SNR/RSS occurs at thefirst device.

In some implementations, the method includes reporting the detectedjamming, whereby other devices coupled to other receivers are informedof the detected jamming. In some implementations, the report is sent toa central authority.

In some implementations, the predicted SNR/RSS is based on predicted LOSdata and predicted NLOS data.

In some implementations, the method includes repeating, at a later time,the actions of receiving, calculating, and comparing. Additionally, thedetecting action is based on an earlier result of the comparing action,and a later result of the repeated comparing action.

Clause Set 3

One implementation of a method of representing distant objects foranalysis of satellite line-of-sight visibility from a grid of pointsincludes constructing a first 3D model of foreground objects thatobscure line-of-sight visibility of satellites from a grid of points,wherein the first 3D model is at a first resolution, where spacing ofgrid points denotes obstruction edges. The method also includesconstructing a second 3D model of background objects that are more thana threshold distance away and that object obscure line-of-sightvisibility of satellites from the grid of points, wherein the second 3Dmodel is at a second resolution that is different from and coarser thanthe first resolution. The method also includes calculating aline-of-sight visibility of the satellites from the grid of points usinga combination of the first and second 3D models. The method alsoincludes responding to a query for an area by providing the calculatedline-of-sight visibility of the satellites for points of the grid withinthe area.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

In some implementations, at least some obstruction edges model buildingedges.

In some implementations, at least some obstruction edges model terrainfeatures.

In some implementations, at least some obstruction edges modelvegetation features.

In some implementations, the first resolution is in a range of 2centimeters (cm) to 5 meters (m). In some implementations, the firstresolution is in a range of 5 cm to 1 m.

In some implementations, the threshold distance is in a range of 400 mto 5 kilometers (km).

In some implementations, the second resolution is in the range of 10 mthrough 50 m.

Clause Set 4

One implementation of a method of efficiently determining visible GNSSsatellite positions in a satellite orbit includes possessing an orbitalsegment representing the transit of a satellite in orbit over time. Themethod further includes possessing a coarse ray angle interval. Themethod further includes possessing a fine ray angle interval. The methodfurther includes possessing a Digital Surface Model (DSM). The methodfurther includes performing a first pass, for each coarse ray angleinterval in the orbital segment, by: propagating a coarse ray between aValid Observable Point (VOP) and points on the orbital segment at arespective coarse ray angle to determine whether the coarse ray isobstructed by features of the DSM, and recording a status of the coarseray with LOS visibility or NLOS visibility based on whether the coarseray was obstructed. The method further includes, for each pair ofsuccessive coarse rays in the first pass, if the successive coarse rayshave different status, then designating the coarse ray with NLOSvisibility for further analysis. The method further includes performinga second pass, for each designated coarse rays, by: propagating, betweenthe designated pair, fine rays between the VOP and points on the orbitalsegment at fine ray angle intervals, and saving results from the secondpass, including an indication of a time at which the LOS to thesatellite becomes obstructed.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features. The reader will understand how features identified in thissection can readily be combined with sets of base features identified asimplementations.

For some implementations, the orbital segment was determined based onfactors of curvature of the Earth, terrain, satellite elevation/azimuth.

For some implementations, the coarse ray angle interval permitsdetection of a 3.3 m high obstruction located 1 km distant from the VOP.For some implementations, the coarse ray angle interval permitsdetection of a 3 m high obstruction located 1 km distance from the VOP.

For some implementations, the fine ray angle interval is based on thechange in satellite position per temporal second.

For some implementations, the status of a coarse ray is saved if it hasLOS and the successive ray transitions to NLOS.

For some implementations, the status of a coarse ray is saved if it hasNLOS and the successive ray transitions to LOS.

For some implementations, for each of the designated coarse rays, thefine rays are incrementally traced or cast, starting from the coarse rayof successive coarse rays that is NLOS, at increments of the fine rayangular interval until either a LOS fine ray is determined or until thesum of fine ray angles exceeds the coarse ray angular increment.

For some implementations, as part of the second pass, fine rays arepropagated on both sides of the coarse ray with NLOS.

A non-transitory computer readable storage medium impressed with programinstructions which, when loaded on a GNSS receiver, configure the GNSSreceiver to perform the methods described herein.

This system implementation and other systems disclosed optionallyinclude one or more of the following features. System can also includefeatures described in connection with methods disclosed. In the interestof conciseness, alternative combinations of system features are notindividually enumerated. Features applicable to systems, methods, andarticles of manufacture are not repeated for each statutory class set ofbase features. The reader will understand how features identified inthis section can readily be combined with base features in otherstatutory classes.

The technology disclosed can be practiced as a system, method, orarticle of manufacture. One or more features of an implementation can becombined with the base implementation. Implementations that are notmutually exclusive are taught to be combinable. One or more features ofan implementation can be combined with other implementations. Thisdisclosure periodically reminds the user of these options.

While the technology disclosed is disclosed by reference to thepreferred embodiments and examples detailed above, it is to beunderstood that these examples are intended in an illustrative ratherthan in a limiting sense. It is contemplated that modifications andcombinations will readily occur to those skilled in the art, whichmodifications and combinations will be within the spirit of theinnovation and the scope of the following claims.

We claim as follows:
 1. A method of reducing starting time for a GlobalNavigation Satellite System (GNSS) receiver that has an impreciseinitial starting location, the method including: requesting startingassistance, by the GNSS receiver, from a Content Delivery Network (CDN)that caches predictive data including first data indicated predicted LOSvisibility from the receiver to individual satellites, wherein therequest includes the imprecise initial staring location; receiving, fromthe CDN, data that includes a first block of the predictive data for theimprecise initial staring location and further adjoining second blocksof predictive data for areas surrounding the imprecise staring location;as a first iteration: determining, by the GNSS receiver, commonlyavailable satellites that have visibility from locations in both thefirst block and the second block; and calculating a first startingposition using weighted values for the satellites, the commonlyavailable satellites having higher weighted value than satelliteswithout visibility in both locations, whereby position uncertainty ofthe first starting position is reduced from the imprecise initialstarting location.
 2. The method of claim 1, further including, as asecond iteration: determining, by the GNSS receiver, additional commonlyavailable satellites that have visibility from both locations in thefirst block and locations in the second block; and calculating a secondstarting position using updated weighted values for the satellites, thecommonly available satellites and additional commonly availablesatellites having higher weighted value than satellites withoutvisibility in both locations, whereby position uncertainty of the secondstarting position is reduced from the first starting position.
 3. Themethod of claim 1, wherein the determining occurs by collating theadjoining blocks.
 4. The method of claim 1, wherein the predictive dataincludes second data indicating predicted NLOS visibility from thereceiver to the individual satellites.
 5. The method of claim 1, furtherincluding: the imprecise staring location and the areas surrounding thestarting location having no common satellites; waiting for a nextposition fix; and repeating the steps of receiving, determining, andchoosing.
 6. The method of claim 1, further including: determiningsatellites that are not commonly available in the first block and thefurther adjoining blocks; and ignoring satellites that are not commonlyavailable.
 7. The method of claim 1, wherein the predictive dataincludes second data indicating predicted NLOS visibility from thereceiver to the individual satellites.
 8. A non-transitory computerreadable storage medium impressed with computer program instructions toreduce starting time for a Global Navigation Satellite System (GNSS)receiver that has an imprecise initial starting location, theinstructions, when executed on a processor, implement a methodcomprising: requesting starting assistance, by the GNSS receiver, from aContent Delivery Network (CDN) that caches predictive data includingfirst data indicated predicted LOS visibility from the receiver toindividual satellites, wherein the request includes the impreciseinitial staring location; receiving, from the CDN, data that includes afirst block of the predictive data for the imprecise initial staringlocation and further adjoining second blocks of predictive data forareas surrounding the imprecise staring location; as a first iteration:determining, by the GNSS receiver, commonly available satellites thathave visibility from locations in both the first block and the secondblock; and calculating a first starting position using weighted valuesfor the satellites, the commonly available satellites having higherweighted value than satellites without visibility in both locations,whereby position uncertainty of the first starting position is reducedfrom the imprecise initial starting location.
 9. The non-transitorycomputer readable storage medium of claim 8, further including, as asecond iteration: determining, by the GNSS receiver, additional commonlyavailable satellites that have visibility from both locations in thefirst block and locations in the second block; calculating a secondstarting position using updated weighted values for the satellites, thecommonly available satellites and additional commonly availablesatellites having higher weighted value than satellites withoutvisibility in both locations, whereby position uncertainty of the secondstarting position is reduced from the first starting position.
 10. Thenon-transitory computer readable storage medium of claim 8, wherein thedetermining occurs by collating the adjoining blocks.
 11. Thenon-transitory computer readable storage medium of claim 8, wherein thepredictive data includes second data indicating predicted NLOSvisibility from the receiver to the individual satellites.
 12. Thenon-transitory computer readable storage medium of claim 8, furtherincluding: the imprecise staring location and the areas surrounding thestarting location having no common satellites; waiting for a nextposition fix; and repeating the steps of receiving, determining, andchoosing.
 13. The non-transitory computer readable storage medium ofclaim 8, wherein the predictive data includes second data indicatingpredicted NLOS visibility from the receiver to the individualsatellites.
 14. A system for reducing starting time for a GlobalNavigation Satellite System (GNSS) receiver that has an impreciseinitial starting location, the system including a processor, memorycoupled to the processor, and computer instructions from thenon-transitory computer readable storage media of claim 8 loaded intothe memory.
 15. A method of providing a fast and accurate convergenceduring startup, the method comprising: beginning at an initial locationwith an initial location estimate, whereby the initial location estimateis inaccurate; requesting GNSS forecast data about cuboids in ageographic volume including the initial location estimate; receiving theGNSS forecast data for the geographic volume and extracting predictedsatellite visibility from the GNSS forecast data for candidate locationsproximate to the initial location estimate; ascertaining, for eachcandidate location, a predicted signal to noise ratio (“predicted SNR”)based on the predicted satellite visibility; receiving GNSS satellitesignals and, based on the received GNSS satellite signals, ascertaininga detected signal to noise ratio (“detected SNR”); comparing thepredicted SNRs with the detected SNR; and determining, based on thecomparing, a probability that the receiver is at the respectivecandidate location.
 16. The method of claim 15, wherein the comparinguses a Kalman filter, and further including generating a Kalman filterby: creating an input vector based on the detected SNR; creating a statematrix based on predicted SNR of each satellite and predicted relationbetween each pair of satellites; wherein predicted inaccuracy andpredicted relation between pairs are derived from the GNSS forecastdata; creating a prediction state matrix based on the input vector andthe state matrix; obtaining a first sample of satellite data and firstmeasurement error; and updating the state matrix using the predictedstate matrix and the first sample of satellite data.
 17. The method ofclaim 15, wherein the geographic volume is a height of not exceeding 1tile.
 18. A non-transitory computer readable storage medium impressedwith computer program instructions to provide a fast and accurateconvergence during startup, the instructions, when executed on aprocessor, implement a method comprising: beginning at an initiallocation with an initial location estimate, whereby the initial locationestimate is inaccurate; requesting GNSS forecast data about cuboids in ageographic volume including the initial location estimate; receiving theGNSS forecast data for the geographic volume and extracting predictedsatellite visibility from the GNSS forecast data for candidate locationsproximate to the initial location estimate; ascertaining, for eachcandidate location, a predicted signal to noise ratio (“predicted SNR”)based on the predicted satellite visibility; receiving GNSS satellitesignals and, based on the received GNSS satellite signals, ascertaininga detected signal to noise ratio (“detected SNR”); comparing thepredicted SNRs with the detected SNR; and determining, based on thecomparing, a probability that the receiver is at the respectivecandidate location.
 19. The non-transitory computer readable storagemedium of claim 18, wherein the comparing uses a Kalman filter, andfurther including generating a Kalman filter by: creating an inputvector based on the detected SNR; creating a state matrix based onpredicted SNR of each satellite and predicted relation between each pairof satellites; wherein predicted inaccuracy and predicted relationbetween pairs are derived from the GNSS forecast data; creating aprediction state matrix based on the input vector and the state matrix;obtaining a first sample of satellite data and first measurement error;and updating the state matrix using the predicted state matrix and thefirst sample of satellite data.
 20. A system for provide a fast andaccurate convergence during startup, the system including a processor,memory coupled to the processor, and computer instructions from thenon-transitory computer readable storage media of claim 18 loaded intothe memory.